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 in speech/audio technology, reinforcement learning, ML infrastructure, or another ML specialization.
1 year of experience with end-to-end Machine Learning processes.
Experience in performance optimization and compiler development.
Responsibilities
Execute solutions in specialized ML areas and contribute to model optimization and data processing.
Implement and optimize collective operations in the XLA TPU backend, from HLO-level transformations to low-level code generation.
Improve compiler scheduling to balance latency, memory pressure, and throughput.
Analyze TPU architecture and interconnect features to enhance scaling performance.
Collaborate with ML performance and research teams to diagnose scaling bottlenecks and optimize critical workloads.
Benefits
Comprehensive health and wellness programs.
Generous paid time off and holidays.
Retirement savings plans with company matching.
Opportunities for professional development and training.
Access to cutting-edge technology and resources.
Full Job Description
info_outline X In most instances, this position requires in-person interviews as part of the hiring process.
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.
Responsibilities
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.
Google is a multinational technology company that specializes in Internet-related services and products. These include online advertising technologies, search engine, cloud computing, software, and hardware. Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University. The company has grown tremendously since then and has become one of the most valuable companies in the world. Google's mission is to organize the world's information and make it universally accessible and useful.
