Senior Research Scientist, Gemini Release Evaluations, DeepMind

🏢 Summary: Lead foundational UX research for generative AI products, shaping long-term product strategy and model behavior. The role partners with research, product, and operations teams to translate user evidence into AI evaluation frameworks, including measures for trust, agency, and hallucination tolerance. 🗂️ Requirements: Bachelor’s degree in Human-Computer Interaction, Psychology, Cognitive Science, related field, or equivalent practical experience, 8 years of experience in UX research, product strategy, or a related consumer technology field, Qualitative research experience, Quantitative research experience 📃 Skills: UX, AI, RLHF, Research, Analytics 🏢 Description: Minimum qualifications • Bachelor's degree in Human-Computer Interaction, Psychology, Cognitive Science, or a related field, or equivalent practical experience. • 8 years of experience in User Experience Research, Product Strategy, or a related field within consumer-facing technology. • Experience in qualitative and quantitative research methods. Preferred qualifications • Experience working on systems such as AI, search, algorithmic feeds, or cloud platforms where the user experience is driven by machine learning. • Experience influencing executive stakeholders and managing product changes through evidence-based narratives. Description Artificial intelligence will be one of humanity’s most transformative inventions. The role focuses on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users, with safety and ethics as priorities. Responsibilities • Identify the next wave of user needs in generative AI. • Move beyond tactical testing to lead foundational research that informs the multi-year product roadmap. • Define the signals used for model tuning through Reinforcement Learning from Human Feedback (RLHF) to ensure the AI’s personality and reasoning align with user expectations. • Partner with Research, Product Management, and Strategy and Operations to integrate user insights into core business and technical strategy. • Develop and scale new frameworks for evaluating AI. • Pioneer ways to measure complex concepts such as hallucination tolerance, creative agency, and collaborative trust that traditional UX metrics often miss.

🏢 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: Lead technical engagement at the intersection of frontier AI model development and public policy, translating model roadmaps into regulatory and advocacy strategies. Partner with research, engineering, policy, regulatory, product, and operations teams to address emerging requirements around training data, transparency, safety, security, copyright, and deployment. 🗂️ Requirements: Bachelor’s degree in Computer Science, Data Science, Machine Learning, Law, Public Policy, or equivalent practical experience, 8+ years in technical product, engineering, research, policy, or regulatory-facing roles, Experience with foundational-model development lifecycles, Experience with model capabilities, training-data architectures, evaluation, and deployment, Experience collaborating with cross-functional teams in technical engineering or research organizations 📃 Skills: AI, GenAI, MachineLearning, FoundationalModels, ModelEvaluation, ModelDeployment, TrainingData, DataArchitecture, Regulation, PublicPolicy, Copyright, Safety, Security 🏢 Description: Minimum qualifications • Bachelor’s degree in Computer Science, Data Science, Machine Learning, Law, Public Policy, or equivalent practical experience. • 8 years of experience in either technical product, engineering, researcher roles, or policy/regulatory-facing roles. • Experience with foundational model development lifecycles, model capabilities, training data architectures, and model evaluation and deployment. • Experience working with cross-functional teams in technical engineering or research organizations. Preferred qualifications • Advanced degree in a technical discipline or public policy. • Experience designing, leading, or implementing high-stakes, complex projects from the ground up. • Experience engaging with regulators and policymakers on issues central to AI model development, including data use, model architecture, safety, security, and compute. • Track record of strong contributions to AI model development as a technologist, researcher, public policy manager, or communications specialist. About the job The GenAI Technical Engagement team translates complex technical model-development roadmaps to support frontier AI public policy and regulatory efforts. The role works collaboratively with cross-functional partners to advance actionable advocacy approaches that align public policy efforts with frontier model development and deployment. You will work across research, engineering, public policy, regulatory, product, and operations teams to anticipate emerging regulations related to AI model development, training data, copyright, safety, and security, and align them with technical and model roadmaps. As a technical subject matter expert, you will collaborate with public policy, regulatory, and frontier AI model-development teams to identify technical needs and goals, guide advocacy strategies early, co-design policy playbooks, and develop external responses to emerging regulations that support model development and deployment objectives. Responsibilities • Bridge AI development and regulation by translating complex AI model-development plans into actionable public policy responses co-designed with internal teams. • Steer cross-functional advocacy by partnering with teams managing public policy responses so advocacy frameworks and playbooks evolve alongside model-development roadmaps. • Advise senior management on emerging regulatory developments, their impact, and recommended solutions based on model-development plans. • Serve as a technical subject matter expert for policy and regulatory teams, guiding internal teams and public policymakers on AI topics such as training-data disclosures and model transparency. • Establish metrics to measure the success and impact of advocacy campaigns on the model-development roadmap and generate insights to optimize campaigns.

🏢 Summary: Technical leadership role managing a team that builds capacity-planning systems for high-throughput cloud storage products and large AI/ML workloads. The position owns strategy and delivery for distributed infrastructure solutions, network optimization, automated capacity adjudication, and cross-functional supply alignment. 🗂️ Requirements: Bachelor’s degree or equivalent practical experience, 8 years of software development experience, 3 years of experience with large-scale infrastructure, distributed systems, networks, compute technologies, storage, or hardware architecture, 3 years of technical leadership experience, 2 years of people management or team leadership experience 📃 Skills: Software, Infrastructure, DistributedSystems, Networks, Compute, Storage, Hardware, CapacityPlanning, AI, ML 🏢 Description: Minimum qualifications • Bachelor’s degree, or equivalent practical experience. • 8 years of experience in software development. • 3 years of experience developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture. • 3 years of experience in a technical leadership role. • 2 years of experience in a people management or team leadership role. Preferred qualifications • Master's degree or PhD in Computer Science or a related technical field. • 3 years of experience working in a complex, matrixed organization. • Strong understanding of AI/ML frameworks. • Experience mentoring executive engineers and other technical leads. • Experience in capacity planning for complex, large-scale distributed systems. About the job Responsibilities • Manage team staffing and career growth, uphold a high-performance bar, and foster an environment to build capacity-planning solutions for high-throughput GCS products. • Own the strategy, scoping, navigation, and outcomes for capacity-planning systems targeting high-throughput scenarios, such as the Bandwidth Highway and Rapid Cache. • Guide the team through ambiguity by breaking down complex challenges, including supply scarcity and dense AI zone typologies, into tractable technical milestones. • Navigate complex relationships across GCS, Networking, Compute, and Data Center teams to align supply agendas and ensure seamless onboarding for massive AI/ML workloads. • Drive engineering efforts to automate capacity adjudication, optimize network resources, and accelerate the time required to onboard new high-throughput customers.

🏢 Summary: Opportunity to build and deploy ML and LLM-powered automation for keywordless Google Search Ads products. The role focuses on improving ad relevance, query understanding, and large-scale optimization while solving complex engineering problems that affect advertisers and users globally. 🗂️ Requirements: Bachelor’s degree or equivalent practical experience, 2 years of Python or C++ programming experience, 1 year of experience in generative AI, reinforcement learning, large language modeling, model processing, or another ML specialization, 1 year of end-to-end machine learning experience, including deployment, evaluation, data processing, or debugging 📃 Skills: Python, C++, AI, ML, LLM, Deployment, Evaluation, Debugging, Algorithms 🏢 Description: Minimum Qualifications • Bachelor’s degree or equivalent practical experience. • 2 years of experience programming in Python or C++. • 1 year of experience with one or more of the following: generative AI, reinforcement learning (e.g., sequential decision making), large language modeling, model processing, or specialization in another ML field. • 1 year of experience with end-to-end machine learning (e.g., model deployment, model evaluation, data processing, debugging). Preferred Qualifications • Master’s degree or PhD in Computer Science or related technical fields. • 2 years of experience with data structures and algorithms. • Experience developing accessible technologies. Description The team builds AI-powered automation for Search Ads. Keywordless Search Ads supports products including Dynamic Search Ads, AI Max, Performance Max (PMax), and Smart Campaigns, helping advertisers connect with users effectively and efficiently. Keywordless Ads is a search-ads automation system that delivers highly relevant ads to users with minimal effort from advertisers. The role involves solving open-ended problems in a changing environment and directly impacting advertisers and users worldwide. Responsibilities • Write product or system development code. • Develop and deploy new ML/LLM models to directly impact Google, advertisers, and users. • Manage complex, open-ended engineering problems related to ad relevance, query understanding, and large-scale system optimization. • 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. • Implement solutions in one or more specialized ML areas, use ML infrastructure, and contribute to model optimization and data processing.

🏢 Summary: Software engineering role focused on developing and maintaining large-scale infrastructure or distributed systems. The position involves writing and reviewing code, improving technical documentation, and debugging product or system issues across hardware, network, and service operations. 🗂️ Requirements: Bachelor’s degree or equivalent practical experience, 5 years of software development experience, 1 year of large-scale infrastructure or distributed systems experience, 1 year of data structures and algorithms experience 📃 Skills: Python, C, C++, Java, JavaScript, Algorithms, Golang, Rust 🏢 Description: Minimum qualifications • Bachelor’s degree or equivalent practical experience. • 5 years of experience with software development in one or more programming languages, such as Python, C, C++, Java, or JavaScript. • 1 year of experience building and developing large-scale infrastructure or distributed systems. • 1 year of experience with data structures and algorithms. Preferred qualifications • Master’s degree or PhD in Computer Science or a related technical field. • Experience developing accessible technologies. • Experience with backend coding languages such as Golang, Rust, or Java. • Experience with ML model coding languages, such as Python. About the job Responsibilities • Write product or system development code. • 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.

🏢 Summary: Staff CAD Engineer role focused on architecting, developing, and deploying the CI2 silicon design environment for next-generation TPU and AI/ML hardware. The position improves silicon development flows, iteration speed, and power, performance, cost, and quality outcomes through advanced tooling and cross-functional collaboration. 🗂️ Requirements: Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, related field, or equivalent practical experience, 10 years of technical leadership experience in critical silicon-design areas, Experience in at least two silicon disciplines, Experience developing and deploying advanced tools and development flows for the silicon lifecycle, Ability to lead design-environment architecture, development, and deployment, Knowledge of silicon development phases and scalable design flows 📃 Skills: Silicon, TPU, CI2, AI/ML, DFT, Synthesis, Verification, Firmware, Architecture, Modeling, Place-and-route, Timing, APIs 🏢 Description: Minimum qualifications • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience. • 10 years of experience in technical leadership focusing on one or more critical aspects of silicon design. • Experience in two or more silicon disciplines, including architecture modeling, logic design, functional verification, DFT, synthesis, place and route, timing analysis, physical verification, or firmware development. • Experience developing and deploying advanced tools and development flows that streamline the silicon lifecycle and improve engineering productivity. Preferred qualifications • Master's degree or PhD in Electrical Engineering, Computer Engineering, or Computer Science, with an emphasis on computer architecture. • 15 years of relevant academic and industrial experience in advanced silicon development. • Experience identifying the need for a silicon methodology change, developing a solution, and successfully deploying it with impact to more than 25 developers. • Experience developing solutions that require bridging knowledge gaps among collaborators. • Experience collaborating in internal or external ecosystem standardization efforts. • Knowledge of both front-end silicon development processes—architecture, logic design, and verification—and back-end processes—logic synthesis, place and route, timing convergence, and physical verification. About the job Shape the future of AI/ML hardware acceleration by driving TPU technology for demanding AI/ML applications. Develop custom silicon solutions and use design and verification expertise to verify complex digital designs, with a focus on TPU architecture and its integration within AI/ML-driven systems. As a Staff CAD Engineer, Silicon Design Environment, collaborate with domain experts across the silicon development spectrum to deliver the development environment for chips powering next-generation cloud systems. Refine ambiguous intent into solutions and rally others to develop or deploy them. Responsibilities • Use knowledge of silicon development phases to define the architecture and lead the development and deployment of the overall CI2 design environment as a coherent system of first-party and third-party tools, industry-standard and internal APIs/data formats, and scalable design flows. • Collaborate with technical leaders across CI2 sites to identify evolutionary and revolutionary tooling and methodology changes that enable convergence on a shared design environment. • Engage with other internal teams to keep CI2 at the forefront of advanced silicon development. • Ensure developed technologies optimize project iteration speed while achieving planned power, performance, cost, and quality metrics. • Partner with tools and methodologies team members to apply procedural and AI-first approaches that improve efficiency.

🏢 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: Lead Google Cloud AI Research engineering teams developing and deploying large-scale generative AI solutions. The role combines technical strategy, ML infrastructure optimization, system design, code development, and people leadership across multiple teams and locations. 🗂️ Requirements: Bachelor’s degree or equivalent practical experience, 8+ years of software development experience, 5+ years leading ML design and ML infrastructure optimization, 3+ years in technical leadership, 2+ years with state-of-the-art GenAI techniques or related concepts, 2+ years of people management or team leadership experience 📃 Skills: Python, C, C++, Java, JavaScript, ML, GenAI, LLMs, Multimodal, Vision, Deployment, Evaluation, Debugging, Finetuning 🏢 Description: Minimum qualifications • Bachelor’s degree or equivalent practical experience. • 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript). • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). • 3 years of experience in a technical leadership role. • 2 years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision). • 2 years of experience in a people management or team leadership role. Preferred qualifications • Master’s degree or PhD in Engineering, Computer Science, or a related technical field. • 3 years of experience working in an organization involving cross-functional or cross-business projects. About the job Software Engineering Managers provide technical leadership for major projects while managing teams of engineers. They manage project goals, contribute to product strategy, develop their teams, and help engineers optimize their code. This role manages engineers across multiple teams and locations, a large product budget, and the deployment of large-scale projects across multiple international sites. The Google Cloud AI Research team addresses AI challenges across industries including technology, healthcare, finance, and retail, conducting research with scientific and real-world impact and collaborating with product teams to bring innovations to customers. Responsibilities • Set and communicate team priorities that support broader organizational goals; align strategy, processes, and decision-making across teams. • Set clear expectations based on individuals’ levels and roles; meet regularly to discuss performance and development, and provide feedback and coaching. • Develop the mid-term technical vision and roadmap for one or more teams; evolve the roadmap for future requirements and infrastructure needs. • Design, guide, and vet system designs; write product or system development code to solve ambiguous problems. • Lead the design of GenAI solutions, optimize ML infrastructure, and guide data preparation and model optimization strategies.

🏢 Summary: Lead manufacturing test engineering for hardware systems, managing a team and developing scalable test strategies, automation, and process improvements. The role addresses complex product failures through RCCA, data collection, and manufacturing-process qualification for server, storage, and networking hardware. 🗂️ Requirements: Bachelor’s degree in Computer Science, Electrical Engineering, or equivalent engineering experience, 10 years of test engineering experience in electronics manufacturing, 8 years of people management experience, Python, Bash, and Shell scripting experience for product-specific test plans and test execution 📃 Skills: Python, Bash, Shell, RCCA, ICT, BSCAN 🏢 Description: Minimum qualifications • Bachelor’s degree in Computer Science, Electrical Engineering, or equivalent engineering experience. • 10 years of experience in Test Engineering in electronics manufacturing. • 8 years of experience in people management. • Experience coding in Python, Bash, and Shell scripting to generate product-specific test plans and execute testing accordingly. Preferred qualifications • Experience in manufacturing, quality, or product engineering for hardware technology and systems. • Experience working with server, storage, and networking products, especially AI and ML servers, racks, and integration. • Experience with test station bring-up, gauge R&R/correlation, and partner or agreement manufacturing (ODM, OEM, JDM). • Experience managing manufacturing test hardware using custom electronics, fixturing, instrumentation, and defining test strategy, test automation, test optimization, and scaling test capacity. • Experience with design for test and structural tests such as ICT/BSCAN, Board- and System-level Functional Tests. • Ability to drive improvement of test systems and processes for scaling test capacity. About the job Join a team developing custom silicon solutions that support direct-to-consumer hardware products. Contribute to next-generation hardware experiences focused on performance, efficiency, and integration, while supporting the technical infrastructure behind data centers, platforms, and networks. Responsibilities • Lead a team; set and communicate individual and team priorities that support organizational goals. • Meet regularly with individuals to discuss performance and development, and provide feedback and coaching. • Apply Root Cause Corrective Action (RCCA) fundamentals and collaborate with teams to analyze failures and resolve issues. • Define and execute data-collection activities for highly complex technical issues. • Drive process design, optimization, and qualification activities for manufacturing processes involving components, parts, or products.