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X Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA; San Francisco, CA, USA.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in C or Python.
- Experience with profiling tools (e.g., gProf, Valgrind, or VTune) for performance optimization.
- Experience in software development for hardware accelerators (e.g., CPUs, GPUs, or TPUs), including memory-hierarchy or instruction-level tuning.
Preferred qualifications: - Master's degree or PhD in Computer Science or related technical fields.
- 3 years of experience with advanced data structures, algorithms, and machine learning optimization.
- Experience developing accessible technologies.
About the jobAt Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team.
Our mission is to maximize the performance and scaling of hardware accelerators (TPUs and GPUs) across Google DeepMind and Google, with a core focus on Gemini model training and serving. We work across the JAX stack-spanning custom kernel development, large-scale model sharding, performance analysis, and compiler optimizations. Operating at the intersection of modeling, compilers, and infrastructure, we collaborate closely with partner teams to deliver end-to-end efficiency.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Improve the training and serving efficiency of Gemini models on hardware accelerators (TPUs and GPUs), spanning model configurations, execution runtimes, and dedicated compiler passes.
- Profile large-scale distributed workloads to diagnose compute, memory, and communication bottlenecks, identifying high-impact optimization opportunities.
- Design and implement high-performance, low-level custom kernels for critical model operators to unlock peak hardware utilization.
- Build and maintain automated benchmarking pipelines and diagnostic tooling to track, reproduce, and guard against performance regressions.
- Influence next-generation accelerator architectures and compiler roadmaps by feeding back empirical workload profiles and model requirements.
