Staff Software Engineer, TPU Performance, CoreML

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
  
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• 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), or specialization in another ML field.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience working with GPU or TPU optimizations.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures/algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• 3 years of experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.).
• 3 years of experience in tailoring algorithms and ML models to exploit ML accelerator architecture strengths and minimize weaknesses.

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.

As a Staff Software Engineer, you will develop ML performance analysis and optimization technology to advance the latest TPU platform to market leading performance.

You will work on Gemini, as well as industry leading open-source models, to understand model architecture and optimize the performance of these ML models on TPU systems for both JAX and PyTorch platforms. You will improve the performance of ever-evolving ML workloads, achieving results. These fundamental efforts will influence next-gen TPU architectures via strategic partnerships, ensuring performance for Gemini and OSS ML models.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Identify and maintain ML training benchmarks that are representative to Google production, industry and ML community, use them to identify performance opportunities and drive TensorFlow (TF)/JAX GPU performance and to gate TF/JAX releases. 
• Engage with Google product team, Cloud, researchers to solve their performance problems.
• Analyze performance and efficiency metrics to identify bottlenecks, design and implement solutions at Google fleetwide scale.
• Explore model/data efficiency techniques, for example, new ML model arch/optimizer/training technique to solve a ML task more efficiently, new techniques to reduce the label/unlabeled ML data needed to train a model to target accuracy.
• Work with tooling and fleet metrics subteams to build tools to track performance and efficiency and to extract metrics from Google running workloads.