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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.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
- 3 years of experience with Computer Architecture, C , Python, CUDA, GPU Programming, GPU Drivers, System On a Chip, Application-Specific Integrated Circuit.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
Preferred qualifications: - Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- 1 year of experience in a technical leadership role.
- Experience with GPU or TPU performance analysis, and a passion for developer productivity.
- Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers such XLA.
- Proven track record in open-source software development, including experience in releasing and supporting open-source projects.
As a part of this team, you will be focused around driving continuous improvements to the machine learning software/hardware stacks through providing insightful performance debugging for workloads and custom kernels. You will provide insights by summarizing different views of captured profile data such as trace timelines, memory usage, Compiler profiles, ML graph summaries.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Write and test product or system development code.
- Learn and build an intuitive understanding of existing data collection, analysis, and visualization workflows.
- Support new and exciting ML paradigms (such as horizontal scaling for upcoming TPU chips) by making contributions across the end-to-end stack and analysis tools.
- Partner with Product Area leads to understand model optimization use cases, drive cross functional efforts to deliver on chip profiling requirements, and propose new hardware features.
- Collaborate across Hardware, Driver, Runtime, and Performance Analysis teams and many other stakeholders.
