The role focuses on designing and implementing software for AI infrastructure, specifically connecting Tensor Processing Unit (TPU) clusters and cloud systems. It involves building and integrating cloud compute software to support AI development and deployment at scale.

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 III role focused on developing GenAI and Vertex-based development tools, including server and middleware code that interacts with LLMs. The role involves integrating next-generation LLMs with a focus on performance and deployment constraints, working closely with AI researchers and product managers.

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.

Senior Software Engineer for Google Home Camera, focusing on the camera stack and On-Device Machine Learning (ODML) framework for AI home security products. Responsible for end-to-end ownership of NPI projects, advancing computing technologies for consumer devices, and providing technical coaching.

Staff Software Engineer for Google Cloud's AI/ML Infrastructure team, focusing on building and operating a multi-tenant ML-as-a-service platform to enable Google product teams to build and launch GenAI-powered products. The role involves simplifying AI workflows, collaborating across front-end, infrastructure, and modeling expertise, and democratizing AI techniques.

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.

Staff Software Engineer at Google Cloud Storage, focusing on building and optimizing storage solutions for AI/ML workloads, bridging core storage infrastructure with the demands of training and inference.

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.

Staff SWE role focused on architecting and owning the accuracy and fidelity of a critical co-design simulation platform for next-generation ML accelerators. The role involves establishing correlation infrastructure between simulated and physical hardware, evolving the simulation layer for complex workloads like LLMs, and identifying system-level bottlenecks at the pre-silicon stage to provide performance estimates for future ML systems.

Customer Engineer focused on AI infrastructure modernization using Google Cloud's TPU/GPU accelerators. The role involves guiding customers on architecture, deployment, and optimization of large-scale training and inference jobs, working with AI/ML accelerators, and supporting sales teams in piloting and deploying these solutions.

Software Engineer III role within Google Ads focusing on implementing ML solutions, utilizing ML infrastructure, and contributing to model optimization and data processing. Requires experience in programming (Python/C++) and ML infrastructure, with a specialization in areas like speech/audio, reinforcement learning, or other ML fields.

Senior Software Engineer role focused on developing firmware and software for Google's custom AI accelerators (TPUs). The role involves designing and building low-level C++ code for embedded micro-controllers on ASICs, co-designing hardware/software interfaces, developing tools for ASIC bring-up and debugging, building simulators, and architecting telemetry systems for monitoring TPUs. This position is crucial for enabling the development and scaling of AI models and infrastructure at Google.

Software Engineering Manager responsible for leading a team that develops system software and networking technologies for GPU-based AI/ML supercomputers in Google's data centers. This includes managing engineers, defining technical roadmaps, and overseeing the integration, testing, deployment, and debugging of system software for accelerator products.

Senior AI System Engineer role focused on influencing Tensor SoC architecture for AI/ML workloads on Pixel devices. This involves cross-functional collaboration with SW/HW architects and research teams to optimize for Power, Performance, and Area (PPA) trade-offs, enabling advanced Generative AI experiences. The role requires a data-driven approach using profiling, simulation, and modeling to define future SoC architectures and system design documentation.

Technical Lead for AI/ML Storage at Google, focusing on managing and optimizing storage solutions for AI/ML workloads. This role involves leading product design, developing AI/ML client libraries, and driving storage innovation across Google Cloud Platform services. Requires extensive software development experience, including building ML solutions and infrastructure, with a focus on performance engineering and the evolving AI/ML landscape.

This role focuses on optimizing the performance of machine learning software and hardware stacks, particularly for TPUs, by providing performance debugging and analysis for ML workloads and custom kernels. The engineer will contribute to the end-to-end stack and analysis tools to support new ML paradigms and partner with teams to deliver chip profiling requirements.

Senior Customer Engineer focused on AI infrastructure, specifically Google Cloud TPUs, for enterprise clients. This role involves designing, deploying, and optimizing AI training and inferencing solutions, advising on ML operations, and supporting sales teams by solving technical challenges related to AI hardware and software stacks.

Technical Lead for Vector Search Serving infrastructure within Google Cloud's Vertex AI platform, focusing on managing massive datasets and query volumes with low latency. The role involves technical leadership, architecture, code reviews, and mentoring engineers, with a requirement for experience in GenAI techniques.

Senior Software Engineer role focused on AI/ML within Google Search, involving the design and implementation of solutions in specialized ML areas, leveraging ML infrastructure, and contributing to product/system development. The role requires experience with ML infrastructure, model deployment, evaluation, optimization, and specific ML fields like Recommendation Systems or reinforcement learning.

Software Engineer role focused on 3D computer vision, specifically camera calibration workflows and algorithms, from conception to factory deployment. Responsibilities include creating system requirements, designing experiments, authoring monitoring dashboards, and managing calibration station bring-up at manufacturing sites. Requires experience with ML infrastructure and computer vision.

This role focuses on developing and deploying AI/ML models within Electronic Design Automation (EDA) CAD tool flows for AI/ML hardware acceleration, specifically Google's TPUs. The engineer will architect, develop, and implement software systems to automate and optimize chip design processes, focusing on improving Power, Performance, and Area (PPA) and Turn Around Time (TAT). The role involves a blend of hardware design, software engineering, and AI/ML application within the silicon development lifecycle.

This role focuses on the RTL design and verification of PCIe subsystems for Google's next-generation Tensor Processing Units (TPUs), which are custom accelerators for AI/ML workloads. The engineer will architect and implement SoC-level RTL, focusing on foundational infrastructure like clocking, reset, and error handling, and collaborate with hardware and software teams throughout the product lifecycle. The position involves leading PCIe microarchitecture, RTL development, integration, and post-silicon bring-up, requiring expertise in ASIC design, PCIe protocols, and verification methodologies.

Software Engineer III role at Google Play focused 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, contributing to documentation, triaging issues, and debugging. Requires experience in Python/C++, and ML infrastructure or a specialization in an ML field like speech/audio or reinforcement learning.

Seeking an Engineering Manager with an infrastructure technical background to lead a team focused on optimizing planet-scale data processing infrastructure. This platform powers foundational AI/ML features and next-generation AI initiatives, with a focus on advancing scheduleability and auto-tuning for ML pipelines and standard data workloads. The role involves leading research into AI-driven optimization strategies and collaborating with teams working on frontier AI models.

This role focuses on architecting the hardware (SoC Vision Architect) for AI/ML applications, specifically Tensor Processing Units (TPUs) and their associated imaging pipelines (ISP, CODECS). The goal is to define custom silicon solutions that power Google's demanding AI/ML workloads, optimizing for power, performance, and area while integrating advanced computational imaging algorithms and potentially deploying neural networks on specialized hardware.

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.

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 Engineering Manager for Content Safety, focusing on AI/ML infrastructure and deployment for protecting users from harmful content. The role involves leading teams, setting technical direction, and ensuring the scalability and reliability of ML systems.

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.

Customer Engineer for Google Cloud AI Infrastructure, focusing on TPU/GPU accelerators for training and inference. The role involves advising customers on AI infrastructure strategy, designing and implementing solutions, performance tuning, and MLOps integration.

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.

Technical Solutions Engineer for Google Cloud AI/ML portfolio, focusing on customer-reported issues, deployment failures, and model performance degradation. Responsibilities include troubleshooting, debugging ML models (TensorFlow, PyTorch) in production environments (Kubernetes, Compute Engine), and ensuring production readiness of generative AI models. Requires Python coding, AI/ML concepts, and networking/system administration experience.

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.

Technical Solutions Engineer for Google Cloud AI/ML portfolio, focusing on customer-facing support, troubleshooting ML deployments (including Generative AI), and ensuring production readiness. Requires strong Python scripting, debugging skills, and experience with ML frameworks and cloud infrastructure.

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.

Senior Software Engineer focused on optimizing AI/ML infrastructure performance across the technical stack, from networking and data storage to ML models, to provide AI developers with a high-performance experience on Google's AI infrastructure. Responsibilities include designing and implementing solutions, optimizing performance, profiling, debugging, and developing tools for AI/ML infrastructure.

Software Engineer III role focused on building and integrating Cloud Compute software to bootstrap TPU AI Infrastructure, including OS image deployment and node-specific software for computation offload, integrating with TPU clusters and cloud infrastructure. The role involves collaboration with TPU Software and Hardware teams and partnering with cross-functional leaders to shape ML infrastructure.

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.