Data Scientist (GenAI Solutions) - Hybrid

🏢 Summary: Lead the data science strategy and production delivery for an advertising marketplace across onsite and offsite channels. The role manages scientists building ranking, bidding, budget, ROAS, and GenAI systems while partnering with Product and Engineering to improve shopper relevance, advertiser outcomes, and revenue. 🗂️ Requirements: Advanced quantitative degree or 10+ years leading large-scale ML/optimization systems, Leadership of data science or applied science teams, End-to-end production science delivery experience, Large-scale ranking and prediction ML expertise, Advertising supply- and demand-side optimization experience, Production LLM or generative AI implementation experience, Mathematics, statistics, causal measurement, and experimentation expertise, Distributed training and serving pipeline experience, Senior executive and cross-functional communication skills 📃 Skills: MachineLearning, DeepLearning, Embeddings, Retrieval, Ranking, Optimization, Experimentation, MLOps, Auctions, Bidding, Pacing, ROAS, LLMs, GenAI, RAG, Statistics, CausalInference, AWS, SageMaker 🏢 Description: Your Opportunity Lead all Ads Data Science across onsite and offsite advertising channels, with accountability for science strategy and production implementation. Lead a team building the intelligence layer for the advertising marketplace across: - Supply-side algorithms: search, relevance, ranking, real-time bidding, bid boosting, premium positioning, and dynamic auctions. - Demand-side algorithms: budgeting, pacing, budget allocation and reallocation, keyword recommendations, bid recommendations, campaign setup and auto-optimization, and advertiser ROAS optimization. - Portfolio management: orchestration of onsite and offsite advertising portfolios to balance shopper experience, advertiser outcomes, and revenue. Define how modern AI, including LLMs, generative AI, foundation models, and multimodal systems, is deployed as durable production systems. Partner with Product, Engineering, and business stakeholders, and represent Ads science to senior leadership. What You’ll Own - Own Ads Data Science across onsite and offsite channels, including strategy, roadmap, model portfolio, delivery, and scientific quality. - Lead, grow, and mentor data scientists; establish standards for modeling, experimentation, MLOps, and research-to-production execution. - Advance applied science in embeddings, representation learning, CTR/CVR prediction, multi-objective ranking, auction and bidding dynamics, budget optimization, ROAS optimization, causal measurement, and marketplace balance. - Define and execute AI and GenAI workstreams, including generative creative and offer assistance, keyword and query recommendations, bid recommendations, automated bidding guidance, campaign setup and optimization copilots, and retrieval-augmented systems for relevance, discovery, and ranking. - Partner with Product and Engineering to deliver production systems that improve shopper relevance, advertiser ROI/ROAS, and advertising performance. - Establish experimentation and decision frameworks linking science investments to customer and revenue impact. - Communicate science strategy, tradeoffs, and recommendations to senior leadership. - Raise external scientific presence through publications, talks, and recruiting at ML, AI, and advertising venues. This is encouraged, not required. What You’ll Need - Advanced degree (M.S., PhD, or equivalent experience) in a quantitative field such as computer science, machine learning, statistics, operations research, applied mathematics, or data science; or 10+ years building and leading large-scale ML/optimization systems. - Experience leading data scientists or applied scientists and owning end-to-end production science delivery, ideally in advertising, search, recommendations, personalization, or marketplace systems. - Hands-on expertise in large-scale ranking and prediction ML, including deep learning, embeddings, retrieval, multi-objective optimization, and online experimentation. - Experience with advertising optimization systems across supply and demand, including auctions, real-time bidding, pacing, budget allocation, keyword and bid recommendations, auto-campaign optimization, and ROAS optimization in real-time constrained-spend environments. - Experience productizing LLMs or generative AI in real systems, such as RAG, agentic workflows, evaluation harnesses, and quality and safety controls; ideally in advertising, search, or ecommerce. - Strong mathematics, statistics, and scientific-method foundation, with high standards for model quality, causal measurement, and production reliability. - Experience operating distributed training and serving pipelines and partnering with Engineering on scalable deployment. - Ability to influence senior executives and cross-functional partners with clear, technically credible communication. Nice to Have - Digital advertising, retail media, or sponsored products experience. - Ecommerce or retail marketplace experience. - Cloud ML platform experience, such as AWS SageMaker or equivalent, and modern GenAI tooling. - Track record of hiring and developing senior scientific talent. What You’ll Get - Base salary range: $198,500–$300,000. Specific salary depends on relevant experience, education, and work location. - Eligibility for 401(k), new-hire and annual equity grants, and potentially an annual bonus for eligible positions. - Medical/Rx, vision, dental, life, disability, hospital indemnity, critical illness, and accident insurance. - Parental leave, family-services benefits, backup dependent care, flexible spending accounts, telemedicine, pet-adoption reimbursement, employee assistance services, and employee discounts. - Unlimited PTO for exempt salary team members, subject to manager approval, plus six paid holidays annually. Paid sick and family leave may be available in accordance with applicable state and local regulations.

🏢 Summary: AI research role focused on developing and evaluating frontier models, building state-of-the-art evaluation datasets, and guiding research projects from design through release. The position includes publishing research, managing timelines and resources, and analyzing how offline evaluations relate to live production performance. 🗂️ Requirements: PhD in Computer Science, related field, or equivalent practical experience, 2 years leading a research agenda, 1 year of data science experience, Experience training, testing, evaluating, and tuning AI models, Experience managing LLM release cycles and timelines 📃 Skills: AI, LLM, Python, Datasets, Evaluation, Testing, Tuning, Coding 🏢 Description: Minimum qualifications • PhD in Computer Science, a related field, or equivalent practical experience • 2 years of experience leading a research agenda • 1 year of experience in a data science field • Experience with AI model training, testing, evaluation, and tuning processes, as well as LLM release cycles and timeline management Preferred qualifications • 2 years of coding experience • 1 year of experience leading research efforts and influencing other researchers About the job Artificial intelligence will be one of humanity’s most transformative inventions. DeepMind is a pioneering AI lab with interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. The work emphasizes widespread public benefit, scientific discovery, safety, and ethics, and offers learning opportunities and varied career pathways for people driven to achieve results through collective effort. Responsibilities • Author research papers to share and generate impact from research results across functions and in the research community • Drive project work by defining data structures, frameworks, designs, and evaluation metrics for research solution development and implementation; identify timelines and obtain needed resources • Identify gaps in existing release evaluations and create new state-of-the-art datasets that push frontier models to their limits • Build a deeper understanding of the relationship between static evaluations and live production traffic

🏢 Summary: Software engineering role focused on building, testing, and maintaining scalable Google Search products, with a strong emphasis on GenAI solutions and ML infrastructure. The position involves full-stack engineering, design and code reviews, production issue resolution, and evaluating GenAI techniques for large-scale user experiences. 🗂️ Requirements: Bachelor’s degree or equivalent practical experience, 5 years of Python or C++ programming experience, 3 years of software product testing, maintenance, or launch experience, 1 year of software design and architecture experience, 3 years of ML infrastructure experience, 1 year of GenAI techniques or GenAI-related concepts experience 📃 Skills: Python, C++, ML, GenAI, LLMs, Multimodal, Vision, Deployment, Evaluation, Optimization, Debugging, Architecture 🏢 Description:

🏢 Summary: Join a new data science team building AI capabilities for a cloud contact-center platform. The role analyzes large datasets to identify customer patterns, outliers, anomalies, and data gaps that affect model performance, while partnering with product, science, and engineering teams to improve and deploy scalable solutions. 🗂️ Requirements: 2+ years of data science experience, 3+ years of SQL, scripting, or statistical software experience, 3+ years of machine learning and statistical modeling experience, Experience applying theoretical models in an applied environment 📃 Skills: SQL, Python, R, SAS, Matlab, MachineLearning, StatisticalModeling, DataAnalysis, Lex, Polly, Lambda, S3, Kinesis 🏢 Description: Description Join a new team building AI capabilities for a cloud-based contact center platform. The platform uses artificial intelligence and AWS services—including Lex, Polly, Lambda, S3, and Kinesis—to support engaging, dynamic, and personalized customer service experiences. As a Data Scientist, you will analyze massive datasets to categorize customer idiosyncrasies, identify outliers, and systematically detect anomalies that materially affect model performance. You will collaborate with senior technical leaders across AWS, trace decisions from raw data through complex models to business metrics, translate defined business problems into data science problems, and apply appropriate assumptions, methodologies, and data science best practices. Experience with machine learning explainability is a plus. The team is at an early stage, offering significant influence over deliverables without operational load from existing models or systems. You will help solve complex engineering and algorithmic problems, shape the technology and product, and design and deliver scalable, resilient systems with a constant customer focus. Key job responsibilities • Categorize customer idiosyncrasies by summarizing how customers use the product differently and identifying impacts on models. • Detect and clean up outliers that significantly affect model outputs, developing mechanisms for downstream consumption. • Deep dive into discrepancies between customer contact-center formulas and model results to determine whether model issues exist or whether results improve on established customer practices. • Assess data gaps by deriving creative features from existing sources and estimating the accuracy benefit of new data streams. A day in the life The team uses agile project management. You will participate in daily stand-ups, work with the product manager on customer issues requiring deep dives, collaborate with scientists on model-performance issues, and partner with software development engineers on deliverables such as automated data ingestion and model deployment. Basic Qualifications • 2+ years of data scientist experience. • 3+ years of experience with data querying languages, such as SQL; scripting languages, such as Python; or statistical/mathematical software, such as R, SAS, or Matlab. • 3+ years of experience with machine learning/statistical modeling data-analysis tools and techniques, including parameters that affect performance. • Experience applying theoretical models in an applied environment. Preferred Qualifications • Experience with Python, Perl, or another scripting language. • Experience in a machine learning or data scientist role at a large technology company. Compensation and Benefits The base salary range is $153,400.00–$207,500.00 USD annually. The compensation package includes sign-on payments and restricted stock units; final compensation is determined by experience, qualifications, and location. Comprehensive benefits include health insurance, dental, vision, prescription coverage, life insurance options, employee assistance and mental health support, flexible spending accounts, adoption and surrogacy reimbursement coverage, 401(k) matching, paid time off, and parental leave.

🏢 Summary: Research-focused Software Engineer role advancing large language model and information retrieval capabilities for Gemini, Search, and related products. The position combines model evaluation, multi-stage training and practical modeling innovations, with deployment of research advances alongside Search and YouTube teams. 🗂️ Requirements: Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, a related technical field, or equivalent practical experience, 3 years of machine learning experience focused on Large Language Models or Information Retrieval, 3 years of software engineering experience using Python, C++, JAX, or PyTorch, Experience implementing multi-stage training pipelines or algorithms 📃 Skills: Python, C++, JAX, PyTorch, LLMs, IR, MachineLearning 🏢 Description: Minimum qualifications • Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience. • 3 years of experience in machine learning, with a focus on Large Language Models (LLMs) or Information Retrieval (IR). • 3 years of experience with software engineering in Python, C++, JAX, or PyTorch programming, including experience implementing multi-stage training pipelines or algorithms. Preferred qualifications • Experience developing Reinforcement Learning (RL) algorithms, automated evaluation systems, or LLM reflection and reasoning frameworks. • Experience with competitive programming, mathematics competitions (e.g., ICPC, IOI, IMO), or a track record of developing novel algorithms. • Experience rapidly prototyping and iterating on complex systems, generative AI models, or multi-stage pipelines. • Experience collaborating with product engineering teams to deploy machine learning models or research innovations into large-scale production environments (e.g., Search, Recommendation Systems). • Track record of learning new software tools, frameworks, and codebases quickly to solve complex technical obstacles. About the job Research-focused Software Engineers create experiments, prototype implementations, and design new architectures for real-world challenges such as artificial intelligence, data mining, natural language processing, hardware and software performance analysis, compilers for mobile platforms, and core search. The role includes large-scale testing and rapid deployment of promising ideas, while contributing to the wider research community through university partnerships and published papers. Responsibilities • Uncover strategic opportunities at the intersection of Gemini, Search, factuality, continual learning, deep research, and domain internalization. • Analyze models and model-driven products beyond current leaderboards; understand complex problem spaces and create new ways to measure ideal behavior. • Use empirical findings to develop practical interventions and modeling innovations that improve models across downstream surfaces. • Collaborate with product teams such as Search and YouTube to scale research innovations into production environments, optimizing for quality and efficiency.

🏢 Summary: Senior software engineering opportunity focused on the Ads DocService infrastructure that stores and serves landing-page, keyword, query, and creative signals for Ads Quality. The role leads the design and technical execution of core landing-page infrastructure, sets technical roadmaps with Ads partners, and improves real-time performance, latency, and resource efficiency. 🗂️ Requirements: Bachelor’s degree or equivalent practical experience, 8 years of C++ programming experience, 5 years of experience testing and launching software products, 5 years of experience building large-scale infrastructure, distributed systems, networks, compute technologies, storage, or hardware architecture, 3 years of software design and architecture experience 📃 Skills: C++, Testing, Infrastructure, Networks, Compute, Storage, Hardware, Architecture 🏢 Description: Minimum qualifications • Bachelor's degree or equivalent practical experience. • 8 years of experience programming in C++. • 5 years of experience testing and launching software products. • 5 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture. • 3 years of experience with software design and architecture. Preferred qualifications • Master’s degree or PhD in Engineering, Computer Science, or a related technical field. • 8 years of experience with data structures and algorithms. • 3 years of experience in a technical leadership role leading project teams and setting technical direction. • 3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects. • Understanding of how large-scale systems work and their components interact. About the job Software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Products operate at massive scale and extend beyond web search. Engineers work on projects critical to business needs, with opportunities to switch teams and projects as the business evolves. Engineers are expected to be versatile, demonstrate leadership, and take on new problems across the full stack. Using technical expertise, you will manage project priorities, deadlines, and deliverables; design, develop, test, deploy, maintain, and enhance software solutions. Ads DocService maintains signal repositories for landing pages, keywords, and creative text to enable Ads Quality. It provides backend processing frameworks to compute, store, and access rich signals used to optimize ads. The team observes and understands every ad destination, including landing pages, keywords, queries, and creative visible to users, and makes this information available to clients. Advertising teams build products across search, display, shopping, travel, video advertising, and analytics, creating trusted experiences between people and businesses through useful ads and effective advertiser tools that deliver measurable results. Responsibilities • Lead the design and technical execution of core landing-page infrastructure capabilities. • Partner closely with Ads teams to understand product requirements and create designs and long-term roadmaps that support them. • Define short- and long-term technical roadmaps for the infrastructure. • Drive pioneering, first-of-its-kind AI and technical features while ensuring robust, production-grade infrastructure. • Optimize real-time performance, latency, and resource footprint across client architectures and use cases. Benefits Benefits at Google.

🏢 Summary: Develop and optimize large-scale data lake pipelines and end-to-end data processing solutions for an international social media platform. The role offers work with modern Big Data technologies, cross-functional global teams, and flexible hybrid work arrangements. Permanent employment includes training, professional development, healthcare, insurance, and additional employee benefits. 🗂️ Requirements: 3–6 months of Python software development experience, Very good SQL knowledge, Practical OLAP database experience, Knowledge of Hive, Spark, HBase, HDFS, Cassandra, and Kafka, Analytical problem-solving ability, Experience in international multidisciplinary teams, Fluent written and spoken English 📃 Skills: Python, SQL, OLAP, Hive, Spark, HBase, HDFS, Cassandra, Kafka 🏢 Description: Job Description WHO WE ARE: Accenture is a leading global professional services company that helps the world’s leading businesses, governments, and other organizations build their digital core, optimize their operations, accelerate revenue growth, and enhance citizen services. We offer solutions and assets across Strategy & Consulting, Technology, Operations, Industry X, and Accenture Song. At the Data & Applied Intelligence hub, we work locally and globally with over 250 Poland-based specialists. Our expertise spans data architecture and sectoral specialization. We collaborate with technology partners such as Microsoft, AWS, Google, Oracle, and Snowflake to deliver data-driven solutions. We provide comprehensive support from strategy definition to solution implementation and development, covering all aspects of Data & Analytics. For more information about Accenture Technology Poland, please visit our website. THE WORK: Develop end-to-end data solutions, including programming and implementation of data processing components. Support the development, maintenance, and optimization of large-scale data lake pipelines for a global social media platform. Analyze complex technical challenges and deliver effective solutions in a problem-solving environment , demonstrating a commitment to constant progress . Create and maintain technical documentation while dedicated to ongoing improvement and knowledge sharing. Collaborate with international, cross-functional teams, showing a passion for personal development and being motivated to embrace new opportunities . Flexible: The work location for this role may include a mix of working remotely (most of the time), onsite at a client or in an Accenture office - depending on specific project circumstances. With all our roles, there is some in-person time for collaboration, learning and building relationships with clients, peers, leaders, and communities. As an employer, we will be as flexible as possible to support your specific work/life needs. WHAT’S IN IT FOR YOU? Work on international projects involving large-scale data processing and modern Big Data technologies. Expand your technical skill set through exposure to complex data engineering challenges and cloud-scale environments. Gain insights and support from experienced data professionals in a collaborative and diverse team. Access opportunities for continuous learning and professional growth while working with innovative technologies. Qualification HERE’S WHAT YOU’LL NEED: 3 - 6 months of experience developing software using Python. Very good knowledge of SQL and practical experience working with OLAP databases. Knowledge of Big Data technologies such as Hive, Spark, HBase, HDFS, Cassandra, and Kafka. Ability to work analytically in a problem-solving environment. Experience working in international, multidisciplinary teams combining technical and business expertise. Fluent English, both written and spoken. Research indicates that some candidates, especially the most diverse ones, may hesitate to apply for positions if they don't meet all requirements. If you believe you possess the necessary skills, even if not meeting every requirement, we wholeheartedly encourage you to submit your application. BONUS POINTS IF YOU HAVE: Experience with Apache Airflow. Familiarity with cloud-based data platforms and distributed data processing environments. Experience supporting the full lifecycle of data engineering solutions, from development to optimization. Research indicates that some candidates, especially the most diverse ones, may hesitate to apply for positions if they don't meet all requirements. If you believe you possess the necessary skills, even if not meeting every requirement, we wholeheartedly encourage you to submit your application. WHAT WE OFFER? Permanent employment contract. Individual support of a People Lead and a specific path of professional development, as well as the possibility of a session with a Coach. A wide training package (soft, technical, and language training offer, access to e-learning platforms, Gallup test, GenAI training, possibility of co-financing courses, and certification). Employee Assistance Program: legal, financial, and psychological consultations. Accenture employees eligible for the Employee Share Purchase Plan automatically become eligible for quarterly dividends if they own company shares. Paid employee referral program. Private medical care and life insurance. Access to the MyBenefit platform, including a wide range of products and services such as the Multisport card. WHAT WE BELIEVE: Accenture does not discriminate against employment candidates based on race, religion, color, sex, age, disability, national origin, political beliefs, trade union membership, ethnicity, denomination, sexual orientation, or any other basis impermissible under Polish law. All our leaders are committed to building a better, stronger, and more durable company for future generations to create positive, long-lasting change. Inclusion and diversity are fundamental to our culture and core values. Our rich diversity makes us more innovative and creative, which helps us better serve our clients and our communities. Our position as a partner to many of the world’s leading businesses, organizations, and governments affords us both an extraordinary opportunity and a tremendous responsibility to make a difference. Sustainability is one of our greatest responsibilities, and we embed it into everything we do and for everyone we work with.

🏢 Summary: Staff Data Scientist role focused on designing, deploying, and operating enterprise-scale AI and machine-learning solutions. The position owns end-to-end model lifecycles, translating business goals into scalable production systems and advancing capabilities in areas such as NLP, LLMs, recommender systems, and low-latency inference. 🗂️ Requirements: 8+ years of data science and machine learning experience, Master’s or PhD in a quantitative field, 6+ years of hands-on Python and SQL experience, Production-grade modular and optimized code development, End-to-end machine learning solution delivery from business requirements, Supervised and unsupervised machine learning experience, Documented model evaluation, performance tracking, and value measurement, NLP solutions for unstructured data deployed to production, Production deployment of LLM solutions, Version control, CI/CD, and MLOps experience 📃 Skills: Python, SQL, NLP, LLMs, MLOps, CICD 🏢 Description: Your opportunity Schwab Data is the centralized organization that manages and enables the use of data as a strategic asset, supporting enterprise analytics, platforms, and data-driven decision-making. Schwab’s AI & Data Science organization delivers production-ready AI and machine learning solutions that drive measurable business outcomes. The team partners with business units to identify high-impact use cases, pilot analytical solutions, and transition successful models into enterprise-level production systems. Its mission is to accelerate adoption of AI as a strategic product capability, ensuring models are scalable, reusable, governable, and continuously deliver value. Description As a Staff Data Scientist, you will drive the design, development, and implementation of AI and machine learning solutions for complex, enterprise-scale challenges. You will bridge advanced research and robust engineering, owning the end-to-end lifecycle of high-impact models and collaborating with business sponsors, development teams, and engineering partners. This role requires a subject-matter expert who can translate advanced analytical techniques, applications, and strategies into practical, production-ready solutions. What You’ll Do • Analyze and interpret big data, extract insights, and produce innovative AI solutions that enable advanced decision-making using current algorithms, techniques, and tools. • Design and build end-to-end machine learning systems, including scalable and maintainable architectures for data ingestion, feature generation, model training, evaluation, deployment, monitoring, and production value measurement. • Partner with business stakeholders to translate high-level objectives into actionable data science and AI solutions for critical business and technology challenges. • Establish engineering best practices for data science, including modular code design, testing, version control, and production readiness. • Lead complex initiatives involving advanced machine learning, recommender systems, real-time and low-latency inference, and other emerging technologies. Required Qualifications • 8+ years of experience in data science and machine learning. • Advanced degree (Master’s or PhD) in computer engineering, statistics, mathematics, physics, chemistry, or a related quantitative discipline. • 6+ years of hands-on experience using Python and SQL to develop production-grade, modular, optimized code. • Proven ability to convert business requirements into end-to-end machine learning solutions delivered against roadmap milestones for multiple lines of business. • Proven experience developing supervised and unsupervised machine learning solutions, supported by documented evaluation metrics, performance tracking, and value measurement. • Experience applying natural language processing techniques to unstructured data and deploying solutions to production. • Practical experience designing LLM solutions, such as retrieval-augmented generation, agent workflows, or fine-tuning, deployed for internal use. • Strong software engineering fundamentals, including version control, CI/CD, and MLOps practices for production deployments. Preferred Qualifications • Strong background in statistics, forecasting, or causal inference. • Hands-on experience architecting machine learning solutions in cloud ecosystems, including GCP, AWS, or Azure. • Experience building, maintaining, and optimizing data pipelines that support machine learning workflows. • Expertise in MLOps and production model monitoring. • Demonstrated mentorship experience, including coaching senior data scientists or engineers and improving team capability through feedback and code quality. • Strong verbal and written communication skills across all organizational levels. • Strong organizational skills, attention to detail, and a desire to continually reevaluate products and processes. • Ability to succeed in a dynamic, fast-moving environment with a positive attitude, solid work ethic, and strong performance record. Compensation In addition to the salary range, this role is eligible for bonus or incentive opportunities. Benefits The hybrid work and flexibility approach balances workplace flexibility, client service, and regular in-person collaboration. • 401(k) with company match and employee stock purchase plan. • Paid vacation, volunteering time, and a 28-day sabbatical after every five years of service for eligible positions. • Paid parental leave and family-building benefits. • Tuition reimbursement. • Health, dental, and vision insurance.

🏢 Summary: Software engineering role focused on building and maintaining large-scale technical infrastructure that powers global services. The position involves product and systems development, design and code reviews, documentation, and debugging issues across hardware, network, and service operations. 🗂️ Requirements: Bachelor’s degree or equivalent practical experience, 2 years of programming experience in C++, Python, or Go, 2 years of experience with large-scale infrastructure, distributed systems, networks, compute technologies, storage, or hardware architecture 📃 Skills: C++, Python, Go, Infrastructure, Networks, Compute, Storage 🏢 Description: Minimum qualifications • Bachelor’s degree or equivalent practical experience. • 2 years of experience programming in C++, Python, or Go. • 2 years of experience developing large-scale infrastructure, distributed systems, or networks; or experience with compute technologies, storage, or hardware architecture. Preferred qualifications • Master’s degree or PhD in Computer Science or related technical fields. • 2 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging. • 2 years of experience with data structures or algorithms in an academic or industry setting. • Experience developing accessible technologies. • Proficiency in code and system health, diagnosis and resolution, and software test engineering. About the job Software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Products handle information at massive scale and extend beyond web search. As a software engineer, you will work on a project critical to business needs, with opportunities to switch teams and projects as the business grows and evolves. Engineers are expected to be versatile, demonstrate leadership, and take on new full-stack problems. The Technical Infrastructure team builds the architecture behind online user experiences. Its work includes developing and maintaining data centers, building next-generation platforms, and keeping networks running to provide users with a fast, reliable experience. The AI and Infrastructure team delivers AI and infrastructure at scale, efficiency, reliability, and velocity for internal teams, cloud customers, and users worldwide. Teams support AI-model development, global computing services, and developer platforms, with work spanning software and hardware, including TPUs, Vertex AI for Google Cloud, global networking, data center operations, and systems research. Responsibilities • Write product or system development code. • Participate in or lead design reviews with peers and stakeholders to select among available technologies. • Review code developed by other developers and provide feedback to ensure best practices, including style guidelines, code check-in, accuracy, testability, and efficiency. • Contribute to existing documentation or educational content and adapt content based on product or program updates and user feedback. • Triage product or system issues and debug, track, and resolve them by analyzing issue sources and their impact on hardware, network, or service operations and quality.