Machine Learning Engineer

🏢 Summary: Mid Full Stack Engineer role focused on building and improving AI-powered enterprise products across frontend and backend systems. The position involves developing web applications, APIs, and production features using modern technologies while collaborating closely with cross-functional teams. Candidates will work with AI development tools and contribute to scalable, high-quality software in a fast-growing environment. 🗂️ Requirements: 2–5 years of experience building production web applications, Knowledge of React, Knowledge of TypeScript or another modern frontend framework, Experience building backend services, Experience building REST APIs, Familiarity with testing and version control, Understanding of web application architecture, Understanding of client-server communication, Problem-solving skills, Attention to code quality, Strong communication skills, Experience using AI development tools, Willingness to collaborate across engineering, product, and design teams 📃 Skills: React, TypeScript, REST, Git, GitHub, Copilot, Claude, ChatGPT, Python, Node.js, AWS, GCP, Azure, Docker, CI/CD 🏢 Description: About Us Many companies want to use AI agents, but these agents often struggle with complex business tasks. We solve this problem by building a platform that understands company knowledge, policies, processes, and workflows, enabling AI to make reliable decisions instead of simply generating answers. Our technology helps businesses automate up to 85% of customer cases, reduce handling time by 20–25%, and complete tasks that previously took hours in just a few seconds. Our mission is simple: to give people better answers everywhere. About the Role We are looking for a Mid Full Stack Engineer who enjoys building products and solving real user problems. In this role, you'll work across both the frontend and backend, collaborating closely with engineers, product managers, and designers to deliver new features and improve existing ones. You'll have the opportunity to work with modern AI technologies, contribute to production systems, and grow your technical skills in a fast-moving environment. If you enjoy learning, shipping high-quality software, and working on products used by enterprise customers, we'd love to hear from you. Responsibilities - Build and maintain product features across both frontend and backend. - Develop user interfaces using modern frontend technologies. - Build and extend backend APIs and business logic. - Collaborate with engineers, product managers, and designers to implement new functionality. - Improve application performance, reliability, and usability. - Write clean, maintainable, and well-tested code. - Participate in code reviews and engineering discussions. - Help monitor production systems and improve features based on user feedback and product data. - Use AI development tools to improve productivity and software development workflows. Requirements - 2–5 years of experience building production web applications. - Good knowledge of React, TypeScript, or another modern frontend framework. - Experience building backend services and REST APIs. - Familiarity with modern software engineering practices, testing, and version control. - Understanding of web application architecture and client-server communication. - Good problem-solving skills and attention to code quality. - Strong communication skills and willingness to collaborate across engineering, product, and design teams. - Experience using AI tools such as GitHub Copilot, Claude Code, ChatGPT, or similar in day-to-day development. - Curiosity, ownership of your work, and a desire to continuously learn. Nice to Have - Experience with Python, Node.js, or both. - Experience working on AI-powered products or LLM integrations. - Familiarity with cloud platforms (AWS, GCP, or Azure). - Knowledge of Docker, CI/CD, or modern deployment workflows. - Understanding of performance optimization and accessibility best practices. What We Offer - B2B contract. - Company stock options. - MacBook Pro for work. - Modern technology stack with opportunities to contribute to open-source projects. - Access to advanced AI development tools, including GitHub Copilot, Claude Code, OpenAI Codex, TypingMind, and MCP Servers. - The opportunity to work on real AI products used by enterprise customers. - A collaborative engineering culture focused on learning, ownership, and continuous improvement. - The chance to grow your career alongside experienced engineers in a fast-growing AI company. Why Join Us Our leadership team brings deep experience in AI and knowledge management. We've raised over $60 million in funding and achieved three consecutive years of 3× growth. We believe great products are built by great teams, and our culture is shaped by these values: - Quality - We care about craftsmanship and building software that lasts. - Momentum - We move quickly, iterate often, and continuously improve. - Accountability - We take responsibility for our work, communicate openly, and support one another. - Hard Work - We believe ambitious goals require dedication and teamwork. - Learning Agility - We stay curious, embrace feedback, and continuously develop our skills. - Adapt and Thrive - We welcome change and see challenges as opportunities to improve. - Win Together - We succeed by collaborating, sharing knowledge, and helping each other grow.

🏢 Summary: Leadership role responsible for defining and executing the product vision and strategy for Containers and Kubernetes observability within a large-scale SaaS platform. The position oversees a team of product managers and drives AI-powered capabilities to help customers operate, secure, and optimize containerized environments. Focused on building scalable, integrated platform features deeply connected with metrics, logs, traces, security, and CI/CD workflows. 🗂️ Requirements: 8+ years B2B SaaS product management experience, 2+ years managing product managers, Deep expertise in containers and Kubernetes ecosystems, Experience building AI/ML-powered product features, Strong technical background working with large-scale data systems, Experience with observability or infrastructure platforms, Experience building platform or shared infrastructure products, Ability to collaborate closely with engineering on scalable systems 📃 Skills: Kubernetes, Containers, Observability, AI, MachineLearning, Telemetry, CI/CD, Metrics, Logs, Tracing, Security, SaaS, Cloud, Infrastructure 🏢 Description: At Datadog, we're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at massive scale—trillions of data points per day—enabling seamless collaboration and problem-solving across Dev, Ops, and Security teams for tens of thousands of customers worldwide. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. The Containers product group sits at the core of Datadog’s Infrastructure business and powers a significant portion of Datadog’s revenue. As modern infrastructure continues to shift toward Kubernetes and containerized workloads, this group is responsible for helping customers operate, secure, and optimize their most critical production environments—reliably and at scale. In this role, you will define the long-term vision and strategy for Datadog’s Containers & Live processes products, spanning Kubernetes observability, container runtime visibility, workload health, and the operational workflows that infrastructure teams rely on every day. You will lead a team of product managers building foundational platform capabilities used by thousands of customers running mission-critical workloads in production. At Datadog, we place value in our office culture—the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Lead and grow a team of product managers responsible for Datadog’s Containers and Kubernetes product portfolio. Own the product vision, roadmap, and execution for containers observability, ensuring the products continue to drive meaningful customer value and significant Infra revenue. Define and drive the next generation of Containers products by applying AI and machine learning to help customers understand, operate, and troubleshoot complex containerized systems automatically. Co-define and build the next generation of containers and Kubernetes capabilities as part of the Datadog unified platform, deeply integrated with metrics, logs, traces, security, and CI/CD workflows. Partner closely with engineering leadership to deliver platform-level capabilities that scale across products and customers. Collaborate with other Datadog product teams (APM, Logs, Network, Cloud, Security, Serverless) to improve cross-product workflows and help customers troubleshoot faster and operate with confidence. Work with go-to-market, sales, customer success, and support teams to shape positioning, packaging, and launch strategies for new Containers features. Represent Containers product strategy internally and externally, helping align leadership around priorities and long-term bets. Who You Are: Experience: 8+ years of B2B SaaS product management experience, with 2+ years managing and developing a team of product managers. Domain Expertise: Deep familiarity with containers and Kubernetes ecosystems, including how modern infrastructure teams build, deploy, and operate production workloads. Leadership: Proven ability to hire, mentor, and scale high-performing product teams while setting clear direction and expectations. Customer Partnership: Experience working closely with enterprise customers operating complex, large-scale infrastructure. Technical Credibility: Strong technical background that enables effective collaboration with senior engineers and earns trust with highly technical customers.AI Product Fluency: Experience incorporating AI or machine learning into customer-facing products, or strong intuition for where AI can meaningfully improve workflows, decision-making, and automation for infrastructure teams. Data & Systems Thinking: Comfort working with large-scale data systems and understanding how telemetry, signals, and models can be combined to generate actionable insights. Platform Thinking: Experience building foundational platform products or shared infrastructure that supports multiple use cases and teams. Product Craft: Strong track record of shipping impactful products by working closely with design and engineering, balancing usability, performance, scale, and trust. Cross-Functional Influence: Ability to lead complex, cross-team initiatives and align stakeholders across product, engineering, data, and go-to-market functions. Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your experience, we encourage you to apply. Benefits & Growth: New hire stock equity (RSUs) and employee stock purchase plan (ESPP) Continuous professional development, product training, and career pathing Intra-departmental mentor and buddy program for in-house networking An inclusive company culture, ability to join our Community Guilds Access to Inclusion Talks, our Internal panel discussions Free, global Spring Health benefits for employees and dependents age 6+ Competitive global benefits Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. #LI-HybridDatadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.The reasonably estimated yearly salary for this role at Datadog is:$234,000—$300,000 USD About Datadog: Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center. Equal Opportunity at Datadog: Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference. Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications. Privacy and AI Guidelines: Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.

🏢 Summary: The role focuses on engineering production-grade AI features such as semantic search, intelligent assistants, and automated risk scoring by integrating external AI providers into a cybersecurity platform. You will own the full lifecycle from prototype to reliable, monitored production services, primarily using Python on Azure. This position emphasizes robust software engineering practices over model research or training. 🗂️ Requirements: 5+ years of professional experience building production systems in Python, Expert-level Python programming skills, Experience with ML frameworks and libraries, Proven experience shipping and operating production software, Experience integrating external APIs in production environments, Strong understanding of software architecture and design patterns, Practical experience with CI/CD pipelines, Experience with containerization using Docker, Understanding of AI/ML concepts including LLMs, embeddings, prompt engineering, RAG, Experience with unit and integration testing and mocking external services, Polish work permit, Professional proficiency in English 📃 Skills: Python, Azure, OpenAI, Anthropic, Docker, CI/CD, FastAPI, Flask, Pinecone, Weaviate, Chroma, .NET, REST, LLMs, RAG, Embeddings, Git 🏢 Description: About the Role As a Senior AI/ML Engineer you will be building AI-powered features at the core of our platform - semantic search, an intelligent assistant, automated risk scoring, and much more. We mainly integrate with external AI model providers (OpenAI, Anthropic, and others) to deliver these capabilities to our clients and internal users. This is a software engineering role, not a data science or ML research position. You won't train models from scratch or publish papers. You'll engineer production-grade systems that leverage AI to solve real cybersecurity problems. If you're a strong Python engineer excited about making AI reliable, maintainable, and impactful — we want to talk to you. We're looking for a versatile software engineer to own the journey from AI prototype to reliable production system: building robust integrations with external AI providers or fine-tuned models, standardizing our Python codebase and release pipelines, and ensuring our AI features meet the same quality bar as any other production service. What You'll Do Ship AI features end to end - from integration design through deployment to monitoring in production. You'll build and maintain Python services that power our AI-driven capabilities on Azure. Build production-ready AI / ML integrations - including retry logic, rate limiting, cost monitoring, fallback strategies, and abstraction layers that allow us to swap between model providers. Turn proofs of concept into reliable software - collaborate with our AI experts (who bring deep ML/data science expertise) to take working prototypes and engineer them into tested, documented, production-grade services. Standardize Python engineering practices - establish and evolve our code organization, testing strategies, CI/CD pipelines, and release processes for AI/ML features. Design clean service boundaries - our platform's core runs on .NET. You'll design and maintain well-defined APIs between the Python/AI layer and the rest of the system. Mentor and share knowledge - help other engineers grow their Python and AI engineering skills, and bring engineering rigor to the broader team's practices. Contribute to architecture decisions - work closely with other teams to shape how AI capabilities evolve within the platform. Who You Are Must-Haves 🔹 5+ years of professional experience building and maintaining production systems in Python. 🔹 Expert-level programming skills and proficiency with common ML frameworks and libraries. 🔹 A track record of shipping and operating software - not just building prototypes. You care about testing, monitoring, logging, and what happens after deployment. 🔹 Experience integrating external APIs in production systems - ideally AI/LLM provider APIs (OpenAI, Anthropic, Azure AI, or similar), but strong API integration experience in any domain counts. 🔹 Solid understanding of software architecture and design patterns - you have opinions about how to structure a Python project and can articulate trade-offs. 🔹 Practical knowledge of CI/CD pipelines, containerization (Docker), and modern development workflows. 🔹 A working understanding of AI/ML concepts - you know what embeddings, LLMs, prompt engineering, and RAG are. You can have a productive technical conversation with an ML specialist without needing every concept explained from scratch. 🔹 Experience with testing strategies - unit tests, integration tests, mocking external services. You instinctively write tests, not as an afterthought. 🔹 A highly adaptable, problem-solving and result-oriented mindset with the ability to work independently and take ownership in a fast-paced and dynamic environment. 🔹 Strong communication skills in English (our working language). 🔹 Willingness to learn continuously and share what you learn with the team. Nice-to-Haves 🔸 Experience with Azure cloud services. 🔸 Familiarity with FastAPI, Flask, or similar Python web frameworks. 🔸 Exposure to MLOps tooling or practices (model versioning, experiment tracking, feature stores). 🔸 Hands-on experience in Document AI / Intelligent Document Processing using traditional models and Generative AI. 🔸 Experience with vector databases (Pinecone, Weaviate, Chroma, or similar). 🔸 Any experience with .NET - helpful for cross-team collaboration, but not expected. 🔸 Background in or interest in cybersecurity as a domain. Your Team You'll join our Synapse team - a small, focused group responsible for all AI/ML capabilities within our platform. The team consists of: -> Team Leader / Architect - provides high-level architectural guidance, planning, and cross-stack delivery. -> AI / ML Engineer - deep expertise in AI/ML and data science. Your counterbalance: they bring the ML knowledge, you bring the production engineering discipline. -> AI / Backend Engineer - transitioning from .NET into AI engineering. A teammate you'll mentor and grow alongside. -> You - the person who bridges the gap between AI prototypes and reliable production systems. Why Join Us? / What We Offer: Competitive salary and benefits package. Remote work options and flexible working hours. Actual impact on the choice and shape of solutions developed. Opportunities for professional growth and development. Training and conference budget. A collaborative, innovative work environment with an iterative agile approach. The role is for six months with the possibility of extension. Both full-time and part-time cooperation are possible.

🏢 Summary: Role focused on designing, building, and deploying AI/ML solutions end-to-end, from data exploration and experimentation to production deployment and MLOps. The position combines Data Science, ML Engineering, and collaboration with business stakeholders to translate data into actionable solutions. You will develop and maintain ML pipelines, monitor models, and contribute to AI standards within the organization. 🗂️ Requirements: Minimum 1.5 years of experience in Data Science, ML Engineering or MLOps, Proficiency in Python, Experience with ML/DL libraries (scikit-learn, pandas, numpy, PyTorch or TensorFlow), Knowledge of SQL, Experience working with structured and unstructured data, Experience building and deploying ML/AI models, Knowledge of Docker, Knowledge of Git or GitHub, Understanding of CI/CD basics, Experience with MLflow, DVC or Weights & Biases, Knowledge of statistics, model validation and A/B testing, English level B2 or higher 📃 Skills: Python, SQL, scikit-learn, pandas, numpy, PyTorch, TensorFlow, Docker, Git, GitHub, CI/CD, MLflow, DVC, Weights&Biases, MachineLearning, MLOps, Statistics, ABtesting 🏢 Description: Chcesz tworzyć rozwiązania AI, które mają realny wpływ na biznes? Szukamy osoby, która swobodnie porusza się między analizą danych, Machine Learningiem i MLOps, a jednocześnie potrafi rozmawiać z biznesem i przekładać dane na konkretne decyzje oraz wdrożenia. Dołączysz do zespołu rozwijającego nowoczesne rozwiązania AI — od eksploracji danych i eksperymentów, po wdrożenia modeli i rozwój standardów AI w organizacji. Wymagania Min. 1,5 roku doświadczenia w obszarze Data Science, ML Engineering lub MLOps Bardzo dobra znajomość Python oraz bibliotek ML/DL (scikit-learn, pandas, numpy, PyTorch lub TensorFlow) Znajomość SQL oraz pracy z danymi strukturalnymi i niestrukturalnymi Doświadczenie w budowie i wdrażaniu modeli ML/AI Znajomość Docker, Git/GitHub oraz podstaw CI/CD Doświadczenie z narzędziami MLflow, DVC lub Weights & Biases Wiedza z zakresu statystyki, walidacji modeli i testów A/B Umiejętność współpracy z biznesem i prezentowania wyników analiz Analityczne myślenie, dobra organizacja pracy i nastawienie na rezultat Język angielski min. B2 Mile widziane Wykształcenie kierunkowe (informatyka, matematyka, data science lub pokrewne) Doświadczenie w pracy z politykami bezpieczeństwa danych i etyką AI Zadania na stanowisku Analiza danych i identyfikacja obszarów do wykorzystania AI oraz ML Projektowanie, trenowanie i wdrażanie modeli Machine Learning Prowadzenie eksperymentów i monitorowanie jakości modeli po wdrożeniu Rozwój pipeline’ów CI/CD oraz konteneryzacja środowisk ML z użyciem Docker Wersjonowanie modeli, danych i kodu oraz dokumentowanie prac projektowych Współpraca z zespołami Data Engineering, Backend i DevOps Prezentowanie wyników analiz oraz rekomendacji dla biznesu Udział w rozwoju standardów i rozwiązań AI w organizacji Co oferujemy Udział w nowoczesnych projektach AI i Data Science Pracę z nowoczesnym stackiem technologicznym Realny wpływ na rozwój rozwiązań analitycznych w organizacji Wynagrodzenia złożone z podstawy i premii - regulaminowej rozliczanej kwartalnie (budżet premii 10% wynagrodzenia zasadniczego kwartalnego) Możliwość doskonalenia zawodowego - finansowane przez firmę szkolenia i podnoszenie kwalifikacji Pakiet benefitów - prywatna opieka medyczna, ubezpieczenia grupowe, możliwość skorzystania z pakietu sportowego (karta Multisport) Dostęp do platformy e-learningowej z bogatą bazą kursów Imprezy integracyjne

🏢 Summary: The offer is for a Software Engineer (Data) role focused on building scalable data infrastructure and production-ready ML systems within an AI-driven environment. The position combines backend engineering, data engineering, and MLOps to design high-throughput pipelines and support AI solutions from experimentation to production. The role involves close collaboration with Data Science teams to shape architecture and engineering standards for next-generation data platforms. 🗂️ Requirements: 4+ years of software engineering experience, Strong proficiency in Python, Experience building scalable production systems, Experience with data-intensive applications and SQL databases, Knowledge of data modeling and query optimization, Experience with Terraform, Kubernetes, Docker or similar tools, Understanding of CI/CD pipelines and Infrastructure as Code, Experience implementing automated testing and clean architecture principles, Ability to productionize ML solutions with Data Science teams 📃 Skills: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker, Airflow, SQL, CI/CD, AWS, Snowflake, DBT, MLOps 🏢 Description: Our client helps small teams power big businesses with the must-have platform for intelligent marketing automation. Customers from over 170 countries depend on the Client’s mix of pre-built automation and integration to power personalized marketing, transactional emails, and one-to-one CRM interactions throughout the customer lifecycle. We’re looking for a Software Engineer (Data) to join our client’s growing AI and Data organization and help build the scalable foundations behind next-generation data and ML systems. In this role, you won’t just be working with data infrastructure; you’ll be shaping the software architecture, engineering standards, and production-ready platforms that power the company’s AI ecosystem. This is more than a traditional backend or data engineering position. You’ll work at the intersection of software engineering, data, and AI, partnering closely with Data Science and AI teams to bridge the gap between experimentation and production. From designing resilient systems to building scalable MLOps pipelines, your work will directly influence how AI solutions are developed, deployed, and scaled across the organization. Key takeaways: Stack: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker Salary : 20 000 - 23 000 PLN gross/month, Contract of employment (+10% annual bonus, 75% Creative Tax) Working model: Hybrid, once a week in the office Location: Krakow, ul. Konopnickiej Recruitment process: Call with MOTIFE recruiter (30 min) Interview with Hiring Manager (45 min) Technical interview, live coding (1h) Cross-functional interview (1h) Responsibilities: Design, develop, and maintain scalable high-throughput data pipelines across complex data ecosystems. Build and optimize data models, schemas, and database structures to ensure long-term scalability and performance. Implement engineering best practices, including automated testing, CI/CD pipelines, and Infrastructure as Code (Terraform). Partner closely with AI and Data Science teams to productionize machine learning solutions and develop scalable MLOps pipelines. Engineer and maintain feature stores and data infrastructure supporting AI-driven initiatives. Monitor, maintain, and improve the reliability and efficiency of containerized environments using Kubernetes, Docker, and Airflow. Ensure platform stability, observability, and operational excellence through proactive system monitoring and health checks. Collaborate cross-functionally with engineering and business stakeholders to translate complex technical concepts into actionable insights. Contribute to the architectural direction and scalability of the organization’s AI and data platforms. Drive the adoption of robust software engineering standards across data and infrastructure projects. Requirements: 4+ years of experience in software engineering, with strong hands-on experience in backend development and building scalable production systems. Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, and maintainable code practices. Experience working with data-intensive applications, databases, and SQL, including data modeling and query optimization. Exposure to modern data engineering, cloud, or infrastructure environments, with familiarity in tools such as Terraform, Kubernetes, Docker, or similar technologies. Understanding of CI/CD pipelines, Infrastructure as Code, and general engineering best practices. Interest in AI/ML ecosystems and willingness to work closely with Data Science and AI teams on productionizing ML solutions. Familiarity with cloud platforms and modern data stack technologies such as AWS, Snowflake, Spark, or DBT is considered a strong plus. Ownership mindset and comfort working in evolving, fast-moving environments where systems and processes are still being built. What we offer: Health Benefits 1. Medical Full coverage for employees and their dependents through LUX MED. Employees have access to the “Premium” package, providing enhanced coverage and greater access to care. A client pays 100% of the premium for employees and 50% for dependents. 2. Dental No additional cost for dental coverage- integrated into LUX MED medical plan. 3. Vision Reimbursement for vision expenses up to 400 PLN every 2 years. Mental Health Tools Access to TELUS Health EAP to provide support and resources in a time of need. Additional Benefits 10% annual bonus 75% Creative Tax Vacation: 26 days. Home Office Stipend: One-time $150 equivalent home office stipend to outfit their home office. Calm Subscription: Premium subscription access to Calm, the #1 app for sleep, meditation, and relaxation. Hub Perks: Receive meal and transportation benefits when traveling to the Poland Hub. Baby Swag: If you have a baby or adopt, you’ll receive a company-branded first bath bundle. Sabbatical Program: After 5 years of employment, receive a month-long paid sabbatical leave, with a sabbatical leave bonus.

🏢 Summary: The role involves owning the full MLOps lifecycle for an AI-powered sports highlights platform, from model research and training to production deployment and scalable cloud infrastructure. You will build and maintain ML pipelines, automate workflows across Computer Vision and NLP, and ensure reliable CI/CD processes in a high-uptime environment. 🗂️ Requirements: 4+ years experience in MLOps, DevOps, ML Engineering or similar role, Experience in large-scale, high-uptime production cloud environments, Strong knowledge of Linux, Experience with Docker, Experience with Kubernetes, Experience building CI/CD pipelines for distributed systems, Proficiency in Python scripting, Experience with Terraform, Understanding of machine learning fundamentals and frameworks 📃 Skills: Python, Linux, Docker, Kubernetes, Terraform, CI/CD, ClearML, W&B, SageMaker, Azure, ComputerVision, NLP, MLOps 🏢 Description: We’re seeking a skilled and motivated MLOps Engineer to join a fast-moving team building an AI-powered platform for automatic sports highlights generation. In this role, you’ll take ownership of the full MLOps lifecycle—from research and model training to production deployment and scalable infrastructure. You’ll design and maintain robust ML pipelines, automate workflows across Computer Vision, NLP, and Data Science, and build reliable CI/CD processes that keep high-uptime systems running smoothly. Your work will help deliver cutting-edge AI experiences at cloud scale, powering innovation and performance across the entire platform. Responsibilities Own and manage every aspect in the MLOps life cycle for our AI-based automatic sports highlights generation platform Design, implement and maintain our whole ML infrastructure, from research to production and from model training to data engineering Automate and innovate workflows such as serving and training pipelines for multidisciplinary ML algorithms including Computer Vision, NLP and Data Science Build and maintain CI/CD pipelines, releases and Source Code workflows Requirements 4+ years of experience as an MLOps engineer, DevOps engineer, ML Engineer, or in a similar field Experience in a large, complex, large-scale, high-uptime production Cloud environment Core understanding of Linux OS, Docker components, and Kubernetes Experience with CI/CD pipelines for distributed production systems Experience with Python scripting Work experience with Terraform Working with MLOps platforms such as Experiment Tracking, Model Registry, feature Store - an advantage (e.g ClearML , W&B, Aws Sagemaker) Highly motivated, goal-driven, innovative, curious, and open-minded Will be a plus Working with Azure Cloud in a high-scale production environment Understanding of AI and machine learning fundamentals, concepts and frameworks What we offer Competitive salary range Medical insurance Paid vacation and sick leaves MultiSport card Top equipment kit, co-workings Hybrid set of works (Office location: Warsaw) Collaborative and innovative work environment Career growth and development opportunities A chance to work with giants of the sports industry About the project Our partner leads the industry in generating dynamic sports videos for every digital destination. Their cutting-edge AI and Machine Learning technologies analyse live sports broadcasts from over 250 leagues and broadcast partners, including iconic names like the NBA, NHL, ESPN, FIBA and Bundesliga, to create personalized, short-form videos in real-time. The solution empowers media rights owners to unlock new revenue streams and deliver a tailored fan experience across every digital platform. Join the high-profile Engineering team and discover the forefront of sports content innovation.

🏢 Summary: AI Engineer role focused on building and deploying ML models, LLM workflows, and AI agents inside a nuclear industry software platform. The position involves production-grade AI engineering, model evaluation, data pipeline development, and collaboration with engineering and domain experts on operational and safety-critical systems. Candidates will work with modern ML tooling, cloud platforms, and agent frameworks to support large-scale nuclear infrastructure projects. 🗂️ Requirements: Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or related technical field, 5+ years of professional experience shipping ML or AI features into production, Strong Python experience, Hands-on experience with modern ML and LLM tooling, Production experience with LLMs and AI agents, Experience with prompting, fine-tuning, RAG, evals, and tool use, Data engineering and data pipeline development experience, Experience working with large datasets, Experience with at least one major cloud platform, Strong written and verbal communication skills 📃 Skills: Python, PyTorch, HuggingFace, LLM, RAG, AWS, MLflow, SageMaker, VertexAI, MCP, Foundry, AIP, CI/CD 🏢 Description: About the role The Nuclear Company is hiring an AI Engineer to ship the machine learning and agent capabilities inside NOS, the software platform behind one of the largest nuclear buildouts in the United States. You'll work in the Platform Integration & AI/Data squad and report to the Director of Platform Integrations. This is a hands-on engineering role. You'll embed ML models and LLM-driven workflows directly into the NOS applications our internal teams and partners use every day: site evaluation, red flag analysis, scheduling, lessons learned, and the stakeholder and project tools that run across active TNC projects. You'll work close to the metal: training and fine-tuning models, designing agent workflows against NOS data and tools, wiring up MCP interfaces, and standing up the evals and monitoring that keep AI features safe in production. You'll partner daily with platform engineers, product engineers, and the nuclear domain experts who use what you ship. The team is small, the pace is fast, and the work is visible, what you build will be demoed to utilities, regulators, and financial partners making 20-to-30-year decisions about the future of American nuclear power. Responsibilities - Ship ML and AI features inside NOS. Train, fine-tune, and deploy models and LLM-based workflows into production, with the evals, monitoring, and safety controls that make them trustworthy. - Build AI agents and agent workflows. Design agents that operate against NOS data and tools, including the MCP interfaces and tool-use frameworks they depend on. - Own model quality end-to-end. Define eval criteria, acceptance thresholds, and regression suites for AI features. Keep results reproducible and the bar high. - Partner across the squad. Work directly with platform engineers and product engineers so models reach production instead of staying in notebooks. - Contribute to the data ontology. Help shape how NOS data is structured so models and agents can use it reliably. - Build predictive and anomaly-detection models that support operations and engineering decisions, including time-series analysis of sensor and operational data from active projects. Required Experience - Bachelor's or Master's in Computer Science, Machine Learning, Statistics, or a related technical field, or equivalent production experience. - 5+ years of professional experience shipping ML or AI features into production. - Strong Python and hands-on experience with modern ML and LLM tooling (PyTorch, Hugging Face, or similar). - Production experience with LLMs and agents: prompting, fine-tuning, RAG, evals, tool use. - Solid data engineering fundamentals, comfortable building data pipelines and working with large, messy datasets. - Experience with at least one major cloud, AWS preferred. - Strong written and verbal communication. Preferred Experience - Experience with Palantir Foundry and AIP. - Experience building agent systems with MCP or similar tool-use frameworks. - MLOps experience (MLflow, SageMaker, Vertex AI) and CI/CD for models. - Experience in a regulated or safety-critical industry: nuclear, aerospace, defense, energy, or medical. - Background in time-series, sensor, or operational data. - Comfortable doing exploratory data analysis and translating findings into product decisions, in addition to shipping production models. Benefits - Competitive compensation packages - 401k with company match - Medical, dental, vision plans - Generous vacation policy, plus holidays Estimated Starting Salary Range The estimated starting salary range for this role is $121,000 - $165,000 annually less applicable withholdings and deductions, paid on a bi-weekly basis. The actual salary offered may vary based on relevant factors as determined in the Company's discretion, which may include experience, qualifications, tenure, skill set, availability of qualified candidates, geographic location, certifications held, and other criteria deemed pertinent to the particular role. EEO Statement The Nuclear Company is an equal opportunity employer committed to fostering an environment of inclusion in the workplace. We provide equal employment opportunities to all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We prohibit discrimination in all aspects of employment, including hiring, promotion, demotion, transfer, compensation, and termination. Export Control Certain positions at The Nuclear Company may involve access to information and technology subject to export controls under U.S. law. Compliance with these export controls may result in The Nuclear Company limiting its consideration of certain applicants.

🏢 Summary: The offer is for a senior GenAI/ML Platform Architect to design and scale an enterprise Digital Ecosystem focused on e-commerce and digital services. The role centers on building and governing RAG-based and agentic AI platforms, establishing LLMOps practices, and enabling secure, compliant, and cost-efficient AI solutions in a global environment. The candidate will lead architecture, lifecycle standards, and integration of multi-agent systems across cloud-native infrastructure. 🗂️ Requirements: 5+ years in MLOps or platform architecture with delivered AI systems, Proficient in Python, Experience with at least one major cloud provider (AWS, Azure, or GCP), Hands-on experience with containers and Kubernetes, Experience with Infrastructure-as-Code tools, Designing scalable ML pipelines for training and deployment, Proven CI/CD implementation for ML/GenAI systems, Experience with RAG architectures and vector databases, Experience with agent orchestration frameworks, Operationalizing multi-agent systems with guardrails and human-in-the-loop, Process automation and enterprise system integrations, Upper-intermediate English level 📃 Skills: Python, AWS, Azure, GCP, Kubernetes, Terraform, CI/CD, MLOps, LLMOps, RAG, LangGraph, SemanticKernel, MCP, VectorDB, Docker, IaC 🏢 Description: Make retail great again through the power of technology! Intellias helps retailers provide consistent and customer-centric shopping experiences across all channels with disruptive retail tech solutions. Get on board and make your own contribution to the industry! Project Overview: Our client is the fastest-growing global manufacturing company. An international corporation with over a hundred years of history, internationally recognized brands and Reduced-Risk Products. Intellias' mission is to support its strategy and efforts in the Digital and e-commerce space (e-commerce and other apps mobile apps, payment gateways, loyalty system, search engine, employee management, identity management, etc.). A newly conceptualized Digital Eco System is comprised of a set of capabilities including an online shop & website, linking online & offline, customization & personalization, engagement & membership, digital product & services main differences. Responsibilities: Lead discovery with stakeholders and define adoption roadmaps and reference architectures Set lifecycle practices for GenAI (LLMOps) Architect retrieval and provider layers (RAG, vector stores, model gateways) with portability, cost, and compliance in mind Implement RAG/agent workflows that orchestrate tool-calling, retrieval, and grounded answering Enable agentic applications at platform level and define solution patterns and evaluation gates (standardized tools, routing, shared memory, HIL, safe fallbacks) aligned with enterprise integration, security, and cost Set standards for ingestion, chunking, embedding, and indexing pipelines; select and tune vector databases for retrieval Establish CI/CD, Infrastructure-as-Code, observability, and automated testing Define governance and safety guardrails Establish environment strategy and promotion paths, and a clear handover plan to client teams Package reusable patterns/accelerators, mentor engineers, and support presales and proposals Requirements: 5+ years in MLOps/platform architecture or adjacent roles, with shipped AI systems Proficient Python and strong software engineering principles Deep experience with at least one major cloud (AWS/Azure/GCP) and platform engineering (containers, Kubernetes, IaC such as Terraform) Experience in designing and guiding scalable machine learning pipelines for model training, validation, and deployment Proven CI/CD design for GenAI/ML (evaluation gates, versioning, canary, rollback) and collaboration with security/governance stakeholders Sound judgement selecting RAG/vector and provider stacks based on performance, cost, compliance, and portability Agent orchestration frameworks (e.g., LangGraph/Semantic Kernel) and tooling protocols (e.g., MCP) Experience operationalizing multi-agent systems (tools/routing/memory/guardrails, human-in-the-loop) Process automation and enterprise integrations Excellent communication and interpersonal skills to collaborate effectively with cross-functional teams, stakeholders' leadership Upper-intermediate level of English Nice to have: Master or higher degree in Computer Science, Engineering, or related field On-prem LLM deployments; performance and cost tuning with caching and model routing AI safety, policy, and compliance experience in sensitive environments Public speaking and enablement and building reusable accelerators Domain exposure in automotive, retail, manufacturing, healthcare, energy, finance, or telecom Perks and Benefits: Flexible work schedule Fixed financial bonus issued upfront on a quarterly basis, covering the average market price of private medical care and sport card - B2B contract Present on the occasion of birthday, wedding, child birth E-learning accounts for Coursera, O'Relly, Udemy Corporate language school

🏢 Summary: Offer for a Manual & Automation QA Engineer to co-create and ensure quality of a long-term HR Tech SaaS platform used by large organizations. The role combines manual testing, test automation, and risk-based analysis of web applications and REST APIs within an Agile product team. You will actively influence product quality by identifying logical gaps, edge cases, and business risks before release. 🗂️ Requirements: Minimum 2 years experience in web application testing, Experience in manual testing, Experience in test automation, Practical experience with Playwright, Ability to create test scenarios and test cases, Experience in REST API testing, Knowledge of DevTools and log analysis, Analytical thinking skills, Ability to work independently, Ability to collaborate with developers and business stakeholders 📃 Skills: Playwright, REST, API, DevTools, SQL, Scrum, Agile, Laravel, React, NodeJS 🏢 Description: Kim jesteśmy? Tworzymy własną platformę SaaS z obszaru HR Tech, z której korzystają duże polskie i międzynarodowe organizacje - w tym firmy technologiczne i liderzy swoich branż. Budujemy rozwiązanie, które realnie wpływa na decyzje biznesowe dotyczące ludzi, rozwoju, ocen pracowniczych, talentów i organizacji pracy. Nie robimy software’u „dla software’u”. Każda funkcjonalność trafia do realnych użytkowników i bardzo konkretnych procesów biznesowych. Pracujemy w oparciu o: Laravel React NodeJS mikroserwisy i API własny produkt rozwijany długoterminowo Nie szukamy osoby, która będzie tylko „odhaczać scenariusze testowe”. Szukamy kogoś, kto potrafi myśleć o produkcie, zadawać niewygodne pytania i przewidywać, co może pójść źle zanim klient to zobaczy. Kogo szukamy? Manual & Automation QA Engineera Osoby, która: potrafi wejść w rolę użytkownika biznesowego, rozumie, że testowanie to analiza ryzyka, a nie tylko sprawdzanie checkboxów, potrafi samodzielnie wyłapać luki w logice produktu, myśli scenariuszowo i biznesowo, chce mieć realny wpływ na jakość produktu. Jeżeli zdarza Ci się podczas korzystania z aplikacji od razu widzieć edge case’y, błędy logiczne albo problemy UX - prawdopodobnie się dogadamy. Czego wymagamy? Must have: minimum 2 lata doświadczenia w testowaniu aplikacji webowych, doświadczenie w testach manualnych, doświadczenie w automatyzacji testów, praktyczna znajomość narzędzi do automatyzacji testów Playwright, umiejętność tworzenia scenariuszy i przypadków testowych, doświadczenie w testowaniu REST API, znajomość DevTools i pracy z logami, umiejętność analitycznego myślenia, samodzielność i odpowiedzialność za dowożenie tematów, umiejętność komunikacji z developerami i biznesem. Mile widziane: doświadczenie z produktami SaaS, doświadczenie w pracy Scrum/Agile, podstawowa znajomość SQL, doświadczenie w testach wydajnościowych. Jak będzie wyglądała Twoja praca? Nie będziesz „testerem na końcu procesu”. Będziesz współtworzyć produkt razem z zespołem developerskim, Product Ownerem i biznesem. Na co dzień: testujesz nowe funkcjonalności platformy, tworzysz i rozwijasz automaty testowe, analizujesz wymagania biznesowe i szukasz potencjalnych ryzyk, uczestniczysz w planowaniu sprintów, pilnujesz jakości produktu zanim trafi do klientów, proponujesz usprawnienia i wychwytujesz problemy logiczne, współpracujesz bezpośrednio z frontendem, backendem i UX. To stanowisko dla osoby, która lubi rozumieć „dlaczego coś działa”, a nie tylko „czy działa”. Co oferujemy? realny wpływ na rozwój produktu, płaską strukturę organizacyjną, szybkie decyzje i brak korporacyjnej polityki, zespół ludzi, którzy naprawdę lubią budować produkt, MacBook + dodatkowe monitory, biuro w centrum Warszawy (ul. Wspólna). Najważniejsze Nie interesuje nas osoba, która „przeklika happy path”. Szukamy kogoś, kto: rozumie produkt, myśli, przewiduje problemy, potrafi wejść w perspektywę klienta, chce budować jakość, a nie tylko raportować błędy. Etapy rekrutacji Analiza CV pod kątem wymagań formalnych: (i) doświadczenie, (ii) technologie; Wstępna rozmowa telefoniczna z wybranymi kandydatami. Wysłanie kwestionariusza dopasowania do zespołu (niezależnie od decyzji Kandydat otrzyma raport po badaniu). Rozmowa online z przedstawicielami działu HR Rozmowa techniczna w biurze z CTO i Product Owner'em Przedstawienie oferty i podpisanie umowy.

🏢 Summary: Leadership role responsible for setting technical vision and delivering AI-powered products across the platform, from research and model development to production operations. The position oversees multiple teams building ML models, agentic systems, and conversational AI solutions at large scale. It combines hands-on AI expertise with organizational leadership to drive end-to-end AI strategy and operational excellence. 🗂️ Requirements: Strong background in AI, Machine Learning, or Data Science, Proven experience building and shipping ML or AI products in production, Experience leading multiple engineering and applied science teams, Deep expertise in LLMs, agentic systems, anomaly detection, time-series modeling, NLP, RAG, or ML infrastructure, Experience owning ML systems from data pipelines to deployment and operations, Ability to define technical strategy and partner with Product, BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field or equivalent experience 📃 Skills: MachineLearning, DataScience, LLMs, NLP, RAG, AnomalyDetection, TimeSeries, MLInfrastructure, ConversationalAI, AgenticSystems, DataPipelines, ModelTraining, ModelDeployment, MLOps 🏢 Description: Datadog's Applied AI group builds AI foundations and AI-powered features across the Datadog platform. We train ML models, build agents, and ship natural language interfaces that help thousands of customers detect problems, investigate incidents, and act on their infrastructure. Our teams work with some of the world's richest observability data, hundreds of trillions of data points per day, to build production AI at real scale. Our work spans ML models, agentic systems, conversational AI, foundation models, and the infrastructure that supports them. As a Manager II, you will lead multiple teams of engineers, applied scientists, and managers in this space. You own the technical direction and the team culture. You drive work from research through production, partner with product to set priorities, and help shape how Datadog uses AI to close the loop between detecting a problem and fixing it. At Datadog, we place value in our office culture—the relationships and collaboration it builds and the creativity it brings. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do: Lead and grow multiple teams of engineers, applied scientists, and managers working on AI-powered features across the Datadog platform Set technical vision and strategy for your area in partnership with Product Ship AI products end-to-end, from data pipelines and model training through evaluation, deployment, and production operations Coach and develop senior engineers and managers, building a culture of high performance, psychological safety, and clear feedback Drive collaboration across Applied AI, product, and partner engineering teams to deliver on Datadog's AI priorities Build evaluation and quality practices for AI systems, offline benchmarks, online metrics, and continuous improvement loops Own operational excellence for your teams' systems: reliability, on-call, incident response, and technical quality Hire and plan for organizational growth as the AI group scales Help shape Datadog's broader AI roadmap and promote best practices in ML engineering and career development across the organization Who You Are: An experienced technical leader with a strong background in AI, machine learning, or data science Proven track record building and shipping ML or AI-powered products in production Experience leading and mentoring multiple teams, including senior engineers and engineering managers Deep technical expertise in one or more areas: large language models, agentic systems, anomaly detection, time-series modeling, NLP, retrieval-augmented generation, or ML infrastructure Comfortable partnering with Product to set vision and strategy, and turning ambiguous problems into clear plans A strong people manager who can attract, develop, and retain top AI and engineering talent BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field, or equivalent professional experience Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you're passionate about technology and want to grow your skills, we encourage you to apply. Benefits and Growth: New hire stock equity (RSUs) and employee stock purchase plan (ESPP) Continuous professional development, product training, and career pathing An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups) Free, global Spring Health benefits for employees and dependents age 6+ Competitive global benefits Giving programs Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.The reasonably estimated yearly salary for this role at Datadog is:$234,000—$300,000 USD About Datadog: Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center. Equal Opportunity at Datadog: Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference. Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications. Privacy and AI Guidelines: Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.