Manager for engineering teams building and deploying AI infrastructure for Google Distributed Cloud and Sovereign Cloud, focusing on GenAI, air-gapped runtimes, and optimizing inference performance in regulated environments.

Engineering Manager for ML Infrastructure, focusing on fleet-wide scheduling for Alphabet's ML workloads. This role involves leading a team, setting technical roadmaps, and ensuring efficient, reliable, and easy-to-use scheduling for ML tasks across production machines, supporting services like Vertex AI and Gemini models.

Senior Staff Software Engineer focused on building and growing the adoption of next-generation bidding technology and products for Search Ads, leveraging ML and Generative AI for optimizations. This includes developing full-stack backend software, implementing sustainable systems and data pipelines, and launching advertiser-facing products and backend improvements.

Senior Data Scientist role focused on Core Ranking and AI Context Engineering (CRAFT) for Google Search, AI Overview, and AI Mode products. The role involves identifying quality and metric headroom, conducting analyses, applying statistical/AI methods, developing and automating evals and measurements for iterative improvements, and partnering with engineering and product teams to drive system changes and launches. The position requires a Master's degree in a quantitative field and 5 years of experience in analytics and coding, with preferred experience in consumer-facing products and evaluation methodologies.

Software Engineer III on the AI and Infrastructure team at Google, focusing on delivering AI and Infrastructure at scale. Responsibilities include writing code, collaborating on design and code reviews, triaging issues, and implementing ML solutions with a focus on ML infrastructure, model optimization, and data processing. Requires experience in Python/C++, ML infrastructure, and a specialization in areas like speech/audio or reinforcement learning.

Software Engineer role at Google YouTube Ads focusing on Machine Learning. Responsibilities include writing product/system development code, collaborating with peers, contributing to documentation, triaging issues, and implementing ML solutions. Requires experience with ML infrastructure, model optimization, data processing, and specific ML areas like speech/audio or reinforcement learning.

Senior Staff Software Architect role focused on the software stack above firmware for Google's AI and HPC infrastructure, specifically concerning distributed systems, Linux OS, networking, and power management for accelerator platforms like GPUs and TPUs. The role involves technical leadership, architecture definition, and driving large-scale technical programs from concept to deployment to enable massive-scale AI and Cloud services.

This role is responsible for coordinating the GenAI serving process and supporting compute needs for model training. The individual will work with teams on their requests, improve and automate workflows, and manage computational power for frontier model development. Key responsibilities include debugging infrastructure, advising on compute governance, identifying launch bottlenecks, and developing internal tooling to streamline processes.

This role focuses on developing and innovating machine learning-based algorithms for video compression, aiming to improve efficiency and reduce complexity for both traditional and generative content. The role involves contributing to industry standards and developing novel algorithms for next-generation codecs, with a specific emphasis on Generative AI applications.

Software Engineer role focused on building and supporting infrastructure components for Google Ads, specifically developing LLM-based agentic AI solutions and related infrastructure. The role involves writing Java code, participating in design reviews, and contributing to documentation for AI agents.

Staff Software Engineer at Google YouTube Ads ML team, focusing on designing, developing, and deploying large-scale ML solutions for advertising products. The role involves technical leadership, optimizing ML infrastructure, and building recommendation system models.

Software Engineer III, AI/ML, Core at Google, focusing on building the technical foundation for AI/ML products. Responsibilities include writing code, collaborating on design and code reviews, contributing to documentation, triaging and debugging issues, and implementing ML solutions with a focus on optimization and data processing. Requires experience with ML infrastructure, model deployment, evaluation, optimization, and data processing, with a specialization in areas like speech, reinforcement learning, or ML infrastructure.

Google is seeking a Software Engineering Manager for the Retrieval Quality team. This role involves defining and driving the technical roadmap, managing a team of engineers, overseeing architectural design, and driving AI transformation to improve engineering productivity and solve technical problems. The team focuses on retrieval infrastructure and search quality, impacting large-scale distributed systems.

Senior Engineering Manager for ML Infrastructure in Ads Safety, leading technical strategy and organizational growth for platforms powering high-stakes content and actor detection. Bridges ML research and production engineering, driving evolution of ML infrastructure for complex models and operational excellence.

Google is seeking an Engineering Manager for their ML Infrastructure Control Plane team. This role involves leading engineering efforts for Alphabet's ML workloads, supporting critical missions across various Google divisions. The team focuses on providing efficient, reliable, and easy-to-use fleet-wide scheduling for ML infrastructure. The manager will set team priorities, develop technical roadmaps, guide system designs, and oversee code development and reviews, ensuring the efficient operation of large-scale ML infrastructure.

Staff Software Engineer focused on building ML compilers for EdgeTPU hardware, optimizing ML models for inference, and working on hardware/software co-optimizations. Collaborates with ML model developers and researchers to deploy models on EdgeTPU.

Software Engineer role focused on developing and optimizing compilers for Google's EdgeTPU, a custom ML accelerator for on-device AI. The role involves analyzing compiler performance, enhancing parallelization algorithms, and efficiently mapping generative AI models to EdgeTPU instructions to enable cutting-edge ML experiences on devices like Pixel phones.

Staff Software Engineering Manager for Emergent AI Infrastructure at Google, focusing on building and managing teams that deliver AI infrastructure at scale with sophisticated security and locality requirements for enterprise customers. The role involves technical leadership, people management, and contributing to product strategy for AI platforms and services.

Tech Lead for Google Kubernetes Engine (GKE) AI Platform, focusing on managing containerized AI/ML workloads on GPU/TPU infrastructure using Kubernetes. The role involves driving innovation in reliability, efficiency, and scale of AI infrastructure, engaging with customers, and leading technical direction for workload optimization.

Senior Software Engineer role focused on building AI/ML features for Google Workspace products, impacting billions of users. The role involves designing and implementing ML solutions, leveraging ML infrastructure, and collaborating with model builders from Google DeepMind to shape the future of productivity tools.

This role focuses on RTL design for AI/ML hardware accelerators, specifically Google's TPUs. The engineer will design and verify complex digital designs, focusing on TPU architecture and its integration within AI/ML systems, contributing to the innovation behind Google's AI/ML applications.

This role focuses on RTL design for AI/ML hardware acceleration, specifically TPUs, powering Google's AI/ML applications. The engineer will design and verify complex digital designs, focusing on TPU architecture and its integration within AI/ML systems, contributing to custom silicon solutions.

Staff Software Engineer focused on optimizing the performance and efficiency of Google's TPU fleet for Machine Learning training and serving workloads, including models like Gemini. This role involves deep analysis of performance metrics, collaboration with product teams and researchers, and implementation of solutions at scale, potentially touching compiler, runtime, model co-design, quantization, and sparsity.

Engineering Manager for Google BigQuery's Advanced Analytics team, focusing on the AI, ML, Search, and extensibility architecture. The role involves developing a technical roadmap for the extensibility platform, enabling custom logic execution within BigQuery for AI/ML pre-processing, training, and inference. It also includes leading feature lifecycles, driving technical vision for hybrid compute workloads, and overseeing production stability and performance.

Software Engineer III, AI/ML role focused on developing LLM-based tools for generating synthetic user data and conversations for health and home feature evaluations. This involves implementing ML solutions, utilizing ML infrastructure, and contributing to model optimization and data processing, with a focus on advancing the quality and realism of synthetic context and defining evaluation metrics.

Customer Engineer II, Applied AI, Google Cloud. This role involves partnering with technical sales teams as an Applied AI subject matter expert to help customers understand and leverage Google Cloud's Generative AI solutions, particularly for customer experience applications. Responsibilities include engaging with customers, understanding their business and technical requirements, developing cloud solutions, conducting Proofs of Concept, and troubleshooting technical issues related to Google's first-party AI offerings. The role requires expertise in conversational AI, building or leveraging AI solutions/ML APIs, and presenting practical solutions to technical and executive stakeholders.

Senior Staff Software Engineer for Google Slides, focusing on integrating GenAI features. This role involves overseeing technical strategy, leading development of AI-powered features using Gemini and Nano Banana, and collaborating with Google DeepMind. The goal is to enhance presentation creation through AI-powered tools, impacting billions of users.

Senior Software Engineer role focused on AI/ML within Google Search, involving the design and implementation of ML solutions, leveraging ML infrastructure, and expertise in specialized ML areas like speech/audio or reinforcement learning. The role requires experience in programming, software development, and ML infrastructure aspects such as deployment, evaluation, optimization, data processing, and debugging.

Software Engineer III at Google Workspace focused on implementing GenAI solutions for productivity tools like Gmail, Docs, and Meet. The role involves utilizing ML infrastructure, contributing to data preparation, optimization, and performance enhancements, and working with core GenAI concepts such as LLMs, Multi-Modal, and Large Vision Models for text, image, video, or audio generation. The primary goal is to build AI-powered features into user-facing products.

Senior Software Engineer role focused on building and deploying recommendation systems, rankings, and predictions models within Google Ads, leveraging ML infrastructure and contributing to architecture design. Requires significant experience in building and deploying recommendation systems and ML infrastructure in production.

Software Engineer III, AI/ML, GenAI at Google. This role involves writing product or system development code, collaborating with peers, contributing to documentation, triaging issues, and implementing GenAI solutions. It requires experience with ML infrastructure, GenAI techniques like LLMs and LVMs, and machine learning frameworks like TensorFlow. The position is focused on engineering and scaling GenAI capabilities within Google's products.

Senior Software Engineer at Google's YouTube, focusing on AI/ML development. The role involves writing and testing code, collaborating with stakeholders, contributing to documentation, triaging and debugging issues, and designing/implementing solutions in specialized ML areas using ML infrastructure. Requires experience in Python, C++, software design, and specific ML fields like speech/audio, reinforcement learning, or ML infrastructure.

Senior Software Engineer on Google Cloud Compute focusing on ML/AI algorithms, deep learning, and NLP. Responsibilities include software development, design, testing, deployment, and maintenance, with a focus on managing project priorities and deliverables. Requires 5 years of software development experience and 3 years of experience with ML/AI algorithms and tools.

Senior Software Engineer focused on optimizing GPU performance for Google's AI models and products. This role involves low-level GPU programming, compiler optimizations, and influencing the GPU software stack and fleet deployment to ensure top performance for ML workloads.

Software Engineer III, AI/ML role at Google YouTube, focusing on implementing ML solutions, utilizing ML infrastructure, and contributing to model optimization and data processing. Requires experience in Python/C++, ML infrastructure, and potentially speech/audio or reinforcement learning.

Software Engineer III on the Google Cloud AI Research team, focusing on implementing ML solutions, utilizing ML infrastructure, and contributing to model optimization and data processing. The role involves writing product/system development code, collaborating with peers, triaging issues, and debugging. Requires experience in Python/C++, and ML infrastructure such as model deployment, evaluation, optimization, and data processing. Experience with speech/audio or reinforcement learning is a plus.

This role focuses on developing architectural and micro-architectural models for next-generation TPUs (Tensor Processing Units) to enable quantitative analysis of performance and power. The engineer will contribute to Machine Learning workload characterization, benchmarking, and hardware-software co-design, collaborating with various teams to define TPU chip specifications and roadmaps for AI/ML hardware acceleration.

Software Engineer III at Google working on AI/ML Recommendations, Rankings, and Predictions for YouTube. The role involves writing product/system code, collaborating with peers, contributing to documentation, triaging issues, and building/deploying recommendation systems models using ML infrastructure, with a focus on model optimization and data processing. Requires a Bachelor's degree, 2 years of Python/C++ experience, 1 year of experience building/deploying recommendation systems, and 1 year of ML infrastructure experience.

Staff Software Engineer at Google working on AI/ML for YouTube Recommendations, Rankings, and Predictions. The role involves designing, developing, and deploying large-scale software solutions, providing technical leadership, and optimizing ML infrastructure. Key responsibilities include leading the design and implementation of recommendation systems, optimizing ML infrastructure, and guiding model architecture development. Requires significant experience in building and deploying recommendation systems models in production and leading ML design and optimization.

Software Engineering Manager II for AI/ML Recommendations, Rankings, and Predictions at YouTube. This role involves leading a team of engineers, setting team priorities, developing technical roadmaps, designing and implementing recommendation systems, optimizing ML infrastructure, and guiding model architecture development. Requires experience in software development, ML design, ML infrastructure optimization, building and deploying recommendation systems, and technical/people leadership.

Senior Software Engineer focused on building and deploying recommendation systems models for YouTube, leveraging ML infrastructure and contributing to architecture design. This role involves coding, testing, debugging, and designing models for retrieval, prediction, ranking, and personalization.