Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Identify and maintain LLM training and serving benchmarks; use them to identify performance opportunities, drive XLA:GPU/Triton performance and guide XLA releases.
Partner with product teams (e.g., Google DeepMind) to onboard, optimize, and scale LLMs and machine learning models on GPU hardware.
Conduct architecture-level simulations, performance benchmarking, and roofline analyses using tools like TRT-LLM, vLLM, and SGLang to guide system designs.
Analyze fleet-wide performance and efficiency metrics to identify bottlenecks and engineer scalable optimizations across Google's infrastructure.
Research and implement model/data efficiency techniques, tooling, and profiling mechanisms to improve workload performance and training efficiency.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience with modern GPU architectures, memory hierarchies, and performance bottlenecks.
Experience with low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.
Experience with modern LLMs and their deployment on AI accelerators.
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.
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.
Experience in hardware-aware algorithm design and compiler stacks (e.g., OpenXLA), tailoring large-scale ML models and distributed systems for peak performance across accelerator hardware.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience with modern GPU architectures, memory hierarchies, and performance bottlenecks.
Experience with low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.
Experience with modern LLMs and their deployment on AI accelerators.
