1 year ago
Base Salary
$238k - $302k/yr
Responsibilities
- Develop neural model architectures and decoding strategies to improve training and inference performance on TPU and GPU architectures.
- Improve ML workload FLOPS efficiency through compiler optimizations, low-level kernels, and low-precision computation.
- Optimize ML systems on TPU and GPU clusters by reducing communication overhead and memory consumption and ensuring distributed scalability and reliability.
- Evaluate and integrate open-source and Google technologies to improve ML workload performance and scalability.
- Promote distributed-systems architecture best practices and contribute technical leadership.
Requirements
- B.S. in Computer Science, Math, or 8+ years of equivalent real-world experience.
- Proficiency in distributed systems design and understanding of ML efficiency.
- Experience with ML frameworks including TensorFlow, JAX, and XLA.
- Solid programming skills in Python and C++.
- Practical familiarity with profiling tools for identifying performance bottlenecks.
- M.S. in Computer Science or Math is preferred.
- Familiarity with Pallas and Triton is preferred.
Benefits
- Eligibility for a discretionary annual bonus program, equity incentive plan, and company benefits program, subject to eligibility requirements.
- Full-time position; remote availability and location-specific salary details are discussed during the hiring process.
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About Waymo
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