Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• 5 years of experience with design and architecture; and testing/launching software products.
• Experience in Python and C programming.

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.
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a matrixed organization involving cross-functional, or cross-business projects.
• Experience in performance analysis and debugging, including for systems that span multiple interconnected hosts.
• Experience in contributing to PyTorch or JAX.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Our Core Machine Learning (ML) team develops the frameworks and compilers that power the Google Cloud Platform (GCP) Cloud Tensor Processing Unit (TPU) service. We provide customers with large-scale, cloud-based access to Google's custom ML supercomputers, enabling them to execute massive training and inference workloads using PyTorch and JAX.

The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving channel behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training. $262000 - $365000 (USD) 25% bonus target bonus equity benefits

Learn more about benefits at Google .

Responsibilities

• Identify and maintain ML training and serving benchmarks that are representative to Google production and the broader ML industry.
• Achieve performance for customer launches, and in case of third-party/OSS models, for engaged benchmark submissions (ML Commons, InferenceX, etc).
• Use the benchmarks to identify performance opportunities and drive both near-term state of the art (e.g. custom kernels) and out-of the box performance (compiler/runtime optimizations, agentic tooling, auto-sharding) directly and in collaboration with partner teams.
• Participate in algorithmic innovations exploiting new TPU hardware features and model-preserving optimizations (speculative decoding, sparsity, quantization, LoRA, etc).
• Participate in co-designing models that are TPU-friendly to showcase model quality at performance excellent to OSS models typically designed on GPUs.