1 day ago
Base Salary
$238k - $302k/yr
Responsibilities
- Design and improve distributed input data pipelines for large-scale machine learning training workloads.
- Collaborate with researchers and ML engineers to resolve data pipeline performance bottlenecks.
- Improve ML training runtime goodput by optimizing input data processing systems.
- Ensure scalability and reliability across distributed environments.
- Implement and maintain ML infrastructure tools including ML Pathways, Grain, JAX, and TensorFlow.
- Evaluate and integrate technologies that improve ML system performance and scalability.
- Promote distributed systems architecture best practices and contribute technical leadership.
Requirements
- Bachelor’s degree in Computer Science or Math, or five years of equivalent real-world experience.
- Proficiency in distributed systems design and understanding of ML data pipeline optimization.
- Experience with ML frameworks including TensorFlow and JAX.
- Hands-on experience with Grain or tf.data service.
- Strong programming skills in Python and C++.
- Practical familiarity with profiling tools for identifying performance bottlenecks.
- Master’s degree in Computer Science or Math preferred.
- Hybrid work arrangement indicated by the #LI-Hybrid designation.
Benefits
- Eligibility for a discretionary annual bonus program.
- Equity incentive plan eligibility.
- Generous company benefits program, subject to eligibility requirements.
- Full-time position with location-specific salary ranges and remote-location compensation details discussed during the hiring process.
Categories
Data EngineeringML Engineering
About Waymo
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