Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages.
- 2 years of experience with computer architecture, performance analysis, and performance modeling.
- Experience experimenting with AI agents or LLM-powered agentic workflows.
- Experience in systems engineering.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- Experience with low-level accelerator programming (e.g., CUDA, Triton, Pallas) and performance tuning techniques.
- Experience with machine learning, compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc).
- Debugging experience to improve performance of single-mode or multi-mode (distributed) systems.
- Expertise in tailoring algorithms and ML models to exploit ML accelerator architecture strengths and minimize weaknesses.
- Deep understanding of modern ML accelerator architectures (Nvidia/AMD GPUs, TPUs, Trainium, Cerebras, etc.), memory hierarchies, and performance bottlenecks.
Responsibilities - Write product or system development code. Manually optimize models, using state-of-the-art techniques (sharding, quantization, custom kernels).
- Develop principles from manual optimizations which can be automated via agentic AI (e.g., compute, communication, scheduling, or quantization cost models).
- Land identified principles and recipes to build scalable solutions, for example, by collaborating with the compiler team on cost modeling or by building agentic tooling for headroom analysis.
- Create scalable solutions to serve the third-party customer base by improving out-of-the-box software performance and open-sourcing tooling to external customers.
- Develop trend analysis to identify performance issues in yet-unseen models, solving performance problems before they occur. While maintaining model benchmarks to ensure tracking of performance wins.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
