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.
Categories
About Waymo
Waymo develops the Waymo Driver, a full-stack autonomous driving system, and operates the Waymo One robotaxi service that offers paid, driverless rides in select U.S. cities. The company monetizes through ride-hailing fares and partnerships integrating its technology on multiple vehicle platforms. Founded in 2009 as Google’s self-driving project, Waymo is headquartered in Mountain View and is an Alphabet subsidiary, with over 10 million rider-only trips and 100+ million autonomous miles on public roads.