Senior Software Engineer focused on building security and privacy frameworks for AI agents within Google Workspace. The role involves developing robust security, enforcing agent identities, and establishing real-time safety guardrails to enable secure collaboration with AI agents, impacting billions of users.

Senior Quality Engineer for Gemini Enterprise Quality at Google Cloud AI Research. This role involves designing and implementing ML solutions, leveraging ML infrastructure, and focusing on quality assurance for AI products, particularly in specialized ML areas like speech/audio or reinforcement learning. The role requires experience in ML infrastructure, including model deployment and evaluation, and contributes to bringing AI innovations to real-world impact.

Senior Software Engineer on the EdgeTPU Compiler team responsible for building next-generation compiler optimizations to power machine learning (ML) models on custom hardware. This role involves translating framework-level code (JAX, PyTorch) into efficient instructions for the EdgeTPU, owning end-to-end features, triaging issues, implementing optimizations, and collaborating with model owners and stakeholders to accelerate ML deployment on edge devices.

Staff Functional Safety Engineer for robotics compute platforms at Google DeepMind, focusing on designing and implementing functional safety architectures for AI-powered robots. The role involves ensuring compliance with safety standards, collaborating with AI research teams, and overseeing the deployment of complex electromechanical systems.

Software Engineer role focused on AI-driven validation of AI-generated code and systems, ensuring stability and correctness as AI code volume grows. Responsibilities include creating and executing integration tests, validating system attributes, and identifying/addressing validation gaps to mitigate risks. The role involves working with coding agents and injecting quality feedback into the development loop.

Tech Lead Manager for Kubernetes AI Infrastructure, responsible for building and managing reliable and scalable TPU orchestration with Kubernetes to enable customers to run GenAI workloads at scale on-premises. The role involves technical leadership, people management, and collaboration with AI labs to influence the infrastructure roadmap.

Google is seeking a Senior Software Engineer to design and implement recommendation systems models for YouTube Ads, leveraging ML infrastructure and contributing to architecture. The role involves writing and testing code, collaborating with stakeholders, triaging issues, and deploying models in production.

Director of Engineering for Google Slides, leading the integration of AI into a collaborative presentation platform. Focus on accelerating innovation, defining the technical roadmap for an AI-first architecture, and partnering with cross-functional teams, including DeepMind, to build advanced AI capabilities and drive commercial success.

Engineering Manager for Google Images, responsible for ranking image results and improving quality through new signals, evaluation methods, and applying research. Leads technical initiatives in areas like Shopping in Images, Personalization, and AI-Organized Search Results. Manages a team of software engineers, guides technical strategy, and oversees development of ranking improvements.

This role focuses on building and scaling ML infrastructure for conversational AI products, including agent assist and dialogflow. The engineer will develop centralized infrastructure solutions to support product teams in areas like compliance, feature development, and scaling, working closely with model builders to accelerate go-to-market for new capabilities.

Software Engineer role focused on the backend infrastructure for Google's Performance Max (PMax) AI-driven advertising platform. The role involves designing, developing, and maintaining highly scalable and reliable systems that automate ad campaigns across various Google products.

Research Engineer role focused on Generative AI for Virtual Try On, involving LLMs and Large Vision Models. Requires significant experience in ML infrastructure, including deployment, evaluation, and fine-tuning, and experience launching software products. The role involves technical leadership and influencing engineering teams.

Senior Staff Uber Technical Lead for Observability Intelligence, driving the strategic shift of SRE incident response to an AI-driven paradigm within Google Cloud's monitoring systems. This role involves leading large-scale ML infrastructure optimization, defining the Observability Intelligence strategy, representing the organization in technical reviews, and partnering with Product Management to translate product needs into scalable architectural solutions. The focus is on building a cohesive, AI-powered observability ecosystem.

Staff Software Engineer at Google working on Search Ads ML and Large Language Models. Responsibilities include designing, developing, testing, deploying, maintaining, and enhancing large scale software solutions, providing technical leadership, and leading the design and implementation of solutions in specialized ML areas, optimizing ML infrastructure, and guiding the development of model optimization and data processing strategies. Requires experience in software development, ML infrastructure, and ML design.

Staff Software Engineer focused on building and optimizing AI infrastructure, including large-scale training and inference workloads, AI clusters, and AI acceleration hardware/networking. The role involves driving technical roadmaps, ensuring end-to-end supportability, and implementing success metrics for the team.

Software Engineer role focused on building AI/ML systems for ad creative optimization at Google, utilizing deep learning, reinforcement learning, and generative AI to enhance relevance and performance for YouTube users.

Staff Software Engineer for YouTube Shorts Creation, focusing on an AI-first editing flow and transitioning to an agentic orchestrator for video editing tools. The role involves technical leadership on Generative AI projects, influencing iOS engineers, and managing project priorities.

Software Engineer AI/ML Networking role focused on building and optimizing AI/ML networking solutions and smart NICs at Google. This involves working at the hardware-software boundary, writing C++ code, and contributing to ML infrastructure for model deployment, optimization, and data processing in high-bandwidth, low-latency environments.

Senior Software Engineer on the Core Engineering Education team at Google, focusing on embedding AI into curriculum and training. The role involves executing technical tasks to develop specialized AI agents and next-generation UI components for seamless human-agent interaction and effective in-the-flow learning, ultimately transforming how Googlers build software by transitioning from manual task execution to agentic orchestration. The team builds foundational technical solutions for Google's products.

Product Manager for GeminiApp, DeepMind, responsible for defining the core rulebook for Gemini's default behaviors, personality, and interaction style. This involves navigating subjective trade-offs, balancing model IQ and EQ, and developing frameworks for evaluating model quality from a taste perspective. The role collaborates closely with research, engineering, and post-training teams to bring the model's personality to life and ensure alignment with product goals and constitutional AI principles.

This role focuses on evaluating AI model performance for health applications, leveraging clinical expertise to guide product development and ensure safety, quality, and efficacy. It involves applying evidence-based practices and contributing to the real-world implementation of AI health products.

Software Engineer role focused on building and deploying ML models, including LLMs, for content safety in Google Commerce. This involves designing, building, and iterating on models for policy violation detection across text, image, and video, as well as developing pipelines for training data curation and model evaluation. The role requires collaboration with various teams to integrate safety measures and analyze data to improve systems. Experience in AI/ML is required, with a preference for generative AI and AI algorithms.

This role involves guiding Public Sector customers in developing, configuring, and deploying data and AI solutions on Google Cloud. The engineer will provide architecture guidance, best practices, and support for customer implementations, including the development and deployment of ML models and integrations. The role also includes customer-facing activities like site visits, solution deployment, and educational workshops, as well as collaboration with Google's Product Management and Engineering teams.

Product Manager for an Automotive AI Agent Platform, focusing on building voice and multimodal interfaces for machine learning systems in the automotive industry. The role involves managing the platform, collaborating with engineering on SDKs for OEMs, understanding customer requirements, defining the roadmap, and bringing new features from research to customers.

Product Manager for YouTube's Growth Platform, focusing on ML ranking algorithms for content promotion to drive user engagement and subscriptions. This role involves leading the ML roadmap, partnering with engineering and data science, and defining principles for content promotions.

Senior Software Engineer focused on AI/ML infrastructure within Google Cloud, involving development, testing, and deployment of ML models and systems at scale. Requires strong programming skills and experience with ML infrastructure or LLM deployment.

Senior Software Engineer role focused on enhancing generative AI models and techniques for Google Ads query understanding. The role involves writing and testing code, participating in design reviews, contributing to ML infrastructure, and identifying/evaluating emerging ML/AI technologies like Gemini models and LLMs to improve user query understanding.

Staff Software Engineer for Travel Ads, focusing on full-stack development, leading system design, and implementing GenAI inference pipelines and AI-native infrastructure. The role involves owning user-facing UI to back-end model infrastructure, collaborating with cross-functional teams, and driving engineering best practices.

This role focuses on building AI-driven systems and automated solutions to detect and mitigate harmful and abusive behavior within the Chrome Web Store and Extensions. The analyst will conduct technical investigations, reverse engineer extensions, and collaborate with engineering and product teams to develop and deploy new detection methods and signals, ultimately enhancing operational workflows through AI integration and process improvements.

Software Engineer, Machine Learning at Google, focused on building and maintaining ML models for Search Advertisements to predict user interactions and optimize advertiser bids. The role involves the full ML model lifecycle, from design and training to deployment and serving in production at scale, utilizing frameworks like Keras and TensorFlow.

Software Engineer III, AI/ML, Search News Intelligence role at Google. Focuses on delivering AI-driven content for Google Search features like Top Stories, News Tab, AI Overviews, and AI Mode. Responsibilities include feature engineering for content understanding and ranking, optimizing ranking performance, deploying advanced signals, and implementing ML solutions using ML infrastructure.

Staff Software Engineer for Google Ads Connect Sales, focusing on AI/ML to empower sales teams. The role involves developing AI-powered products for the entire sales workflow, from lead generation to post-call summarization, by owning the core conversational understanding platform. This position requires technical leadership in designing, developing, and deploying large-scale software solutions, with a focus on Generative AI techniques and ML infrastructure optimization.

Software Engineer role focused on building and advancing ML data and training infrastructure to enable ML use cases for recommendation systems. Requires experience in software development, ML models, and ML infrastructure, including data processing, model optimization, evaluation, and deployment.

This role focuses on developing custom silicon solutions for Google's direct-to-consumer health products, involving the interpretation of health-related data from wearable sensors using machine learning/deep learning models. The engineer will also design, build, and validate new wearable sensors and collaborate on user studies, particularly for Software as a Medical Device (SaMD).

This role focuses on combating abuse within Google Workspace's agentic framework, building data pipelines and deploying models to identify and block malicious actors. It involves working with AI/ML models to protect users and the platform from various forms of abuse, particularly in the context of emerging agentic workflows.

Google's YouTube Discovery Data team is seeking a Staff Software Engineer to build and maintain large-scale data processing pipelines that power personalized discovery and ML models at YouTube. The role involves enabling next-generation model architectures and training procedures, reducing complexity in ML training infrastructure, and collaborating with other infrastructure teams. The ideal candidate will have extensive experience in C++ programming, large-scale infrastructure development, and a solid understanding of ML concepts.

This role focuses on optimizing the performance of AI models (like DeepSeek, Qwen, Gemini, Gemma) on TPUs at Google's scale. It involves fleet-wide analysis, building scaling automation, and last-mile optimization using techniques such as sharding, quantization, and sparsity, while maintaining model quality. The role engages with product teams and researchers to solve performance problems, requiring deep understanding of model design, performance analysis, coding, compilers, and hardware.

Software Engineer role focused on optimizing LLM serving infrastructure and GPU performance, including disaggregated serving, KV cache mechanisms, resource allocation, and performance analysis tools. Collaborates with research and engineering teams to deploy LLMs efficiently.

Staff Software Engineer role focused on integrating and optimizing AI/ML services and models onto Google Distributed Cloud (GDC) platforms, including disconnected and air-gapped environments. Requires significant experience in software development, ML design, and ML infrastructure, with a focus on deployment and evaluation.

Software Engineer III role focused on building AI-driven risk management solutions for Google Payments, leveraging Generative AI and Machine Learning to combat online payment fraud. The role involves developing and implementing GenAI solutions, utilizing ML infrastructure, and contributing to data preparation and optimization to reduce losses and improve fraud detection speed.

Senior Software Engineer role focused on AI/ML within Google Cloud AI. The role involves designing and implementing solutions in specialized ML areas, leveraging ML infrastructure, and contributing to product/system development. Key areas include speech/audio, reinforcement learning, ML infrastructure, model deployment, evaluation, optimization, data processing, and debugging.

Senior Software Engineer focused on performance and infrastructure for AI at Google. This role involves developing and optimizing systems that support AI models at scale, ensuring efficiency, reliability, and velocity. The engineer will write and test code, participate in design reviews, review code from others, contribute to documentation, and triage/debug system issues. The role requires experience in C++ or Python, performance analysis, and large-scale systems, with a focus on the infrastructure that powers AI and Google's product portfolio.

Software Engineer III on the AI and Infrastructure team at Google Cloud, focusing on delivering AI and Infrastructure at scale. Responsibilities include writing product/system code, collaborating on design/code reviews, contributing to documentation, triaging/debugging issues, and implementing solutions in specialized ML areas, utilizing ML infrastructure, and contributing to model optimization and data processing.

Product Manager for Omni-Channel Gateway, focusing on generative AI customer experiences and agentic orchestration. The role involves defining product strategy, forging partnerships, and guiding high-throughput backend systems from ideation to enterprise scale, with a focus on integrating human agents, virtual agents, and enterprise systems.

This role focuses on building and evaluating ML models for business growth, specifically using causal inference and deep learning techniques. The responsibilities include designing, developing, and validating causal inference models, partnering on A/B tests, prototyping new methods, communicating technical findings to executives, and establishing monitoring systems for deployed models. The role aims to integrate ML models into production serving systems and ensure their ongoing accuracy.

This role focuses on defining and delivering high-quality audio experiences across Google's Pixel ecosystem, integrating AI/ML technologies like LLMs, NLP, ASR, and TTS into consumer electronic devices. The engineer will lead system audio architecture, execute experiments, translate concepts into technical specifications, and drive solutions across hardware and software boundaries, collaborating with cross-functional teams and integrating research into products.

Senior Software Engineer on the Pomelli team, responsible for building high-performance web interfaces and prototyping advanced AI features for a marketing platform for small businesses. The role involves taking broad concepts and turning them into functional products, making critical architectural choices, and working at the intersection of web design and AI. The team is part of Google Labs, focused on incubating early-stage efforts.

Research Data Scientist for Chrome, focusing on integrating GenAI to create a more helpful and adaptive agent, personalize user experiences, and drive data-informed innovation using advanced statistical and ML techniques, predictive modeling, and experimentation.