Staff Software Engineer, ML Performance, GPUs
Job Summary
Job Description
- 8+ years software development experience
- 5+ years ML design & infrastructure optimization
- Experience with GPU programming & performance analysis
- Experience with model deployment & evaluation
- Strong data structures & algorithms knowledge
- Master's/PhD in related field
- Experience with TensorFlow or other ML tools
- Compiler optimization experience
- Experience in complex organizations
- Architecture analysis and optimization
- Bonus
- Equity
- Benefits
10 skills required
Job Details
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development, and with data structures/algorithms.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience with performance analysis and GPU programming.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, a related technical field, or equivalent practical experience.
- 5 years of experience working in a complex, matrixed organization.
- Experience with machine learning systems (e.g., background theory, TensorFlow, or other ML tools).
- Experience working on compiler optimizations or related fields.
- Experience with architecture analysis and optimization.
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.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
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 .
Responsibilities
- Analyze Large Language Model (LLM) performance and optimizations for partner teams including Google Gemini, Search, Cloud LLM and Application programming interfaces (APIs).
- Identify and maintain LLM training and serving benchmarks, and use them to identify performance opportunities and drive Accelerated Linear Algebra (XLA):GPU/Triton performance and to guide future XLA releases.
- Engage with Google Product teams, to solve their ML model performance challenges, including onboarding new LLM models and products onto Google’s GPU hardware and enabling LLMs to train efficiently on a very large scale (i.e., thousands of GPUs).
- Run architecture-level simulations on GPU designs and perform roofline analysis to guide partner teams.
- Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions.
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