info_outline
X Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
San Bruno, CA, USA; Mountain View, CA, USA.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 8 years of experience programming in C or Python.
- 5 years of experience testing, and launching software products.
- 3 years of experience with software design and architecture.
- Experience with state-of-the-art ML compilers and their internals, experience writing compiler optimization passes.
- Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers (e.g., accelerated linear algebra (XLA))
Preferred qualifications: - Master's degree or PhD in computer science or related technical fields.
- Experience developing accessible technologies.
- Experience with debugging correctness and performance issues at all levels of the ML software stack.
- Familiarity with accelerator HW architectures (TPUs/GPUs).
About the jobWith your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
On this team, you will own the optimization of the models powering the YouTube algorithm. Your work will range from contributing to the Accelerated Linear Algebra (XLA) compiler for Google's custom Tensor Processing Units, to authoring custom Pallas kernels in JAX, maximizing fleet utilization and the value delivered to users.
You will build support and optimize new and existing models in our RecSys stack, including new model architectures while adapting to next-generation TPU hardware.
You will engage in state-of-the-art model and TPU compiler co-design, with opportunities to work up and down the stack ranging from end-user ML models down to Hardware/Software architecture.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Contribute to the compiler for a novel processor designed to accelerate machine learning workloads.
- Target and compile high-performance implementations of operations at distributed scale.
- Design and implement new compiler passes that extract more performance out of current and next-generation TPUs for our unique LEM (Large Embedding Models) requirements, directly impacting fleet efficiency.
- Collaborate closely with YouTube's Next Platform Evaluation team and Google's hardware designers to co-design future processors.
