US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Execute solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
Implement and optimize collective operations (e.g., all-reduce, all-gather, reduce-scatter, all-to-all) in the XLA TPU backend, from HLO-level transformations to low-level code generation.
Improve compiler scheduling to overlap communication with computation, balancing latency, memory pressure and throughput.
Analyze current and upcoming TPU architecture and interconnect features and use them to improve scaling performance.
Work with ML performance and research teams to diagnose scaling bottlenecks and reach roofline performance on critical workloads.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
2 years of experience in low level systems programming languages (e.g. C++ or C).
1 year 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), ML infrastructure, or specialization in another ML field.
1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
Experience in performance optimization and compiler development.
Preferred qualifications
Master's degree or PhD in Computer Science or related technical fields.
2 years of experience with data structures and algorithms.
Experience developing accessible technologies.
Experience with Hardware Design, Hardware Architecture.
Bachelor’s degree or equivalent practical experience.
2 years of experience in low level systems programming languages (e.g. C++ or C).
1 year 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), ML infrastructure, or specialization in another ML field.
1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
Experience in performance optimization and compiler development.
