Senior ML Infra Engineer — Distributed Data Pipelines

Waymo; California (MO); Hybrid; USD 238,000 - 302,000; Full time

Job summary

Waymo is hiring for a senior ML infrastructure engineer to design and optimize distributed input data pipelines for large-scale ML training workloads. You will collaborate with researchers and ML engineers to remove bottlenecks and improve runtime goodput across distributed environments.

The role requires strong Python/C++ skills, experience with TensorFlow and JAX, and familiarity with modern ML infrastructure tools. This position is hybrid within the US.

Qualifications

• B.S. in Computer Science or Math or 5+ years of real-world experience.
• Proficient in distributed systems design with an understanding of ML data pipeline optimization.
• Experience with ML frameworks, including TensorFlow and JAX.
• Hands-on experience with libraries like Grain or tf.data service.
• Solid programming skills in Python and C++.
• Familiarity with profiling tools to uncover performance bottlenecks.

Responsibilities

• Design, and improve distributed input data pipelines for large-scale ML training workloads.
• Collaborate with researchers and ML engineers to resolve bottlenecks in data pipeline performance.
• Improve runtime goodput of ML training workload, including optimizing input data processing systems, ensuring scalability and reliability across distributed environments.
• Implement and maintain advanced ML infrastructure tools, including ML Pathways, Grain, JAX, and TensorFlow.
• Evaluate and integrate modern technologies to enhance the performance and scalability of ML systems.
• Promote best practices for distributed systems architecture and contribute to technical leadership within the team.

Skills

distributed systems design

ML data pipeline optimization

TensorFlow

JAX

Grain

tf.data service

Python

C++

profiling tools

Education

B.S. in Computer Science or Mathematics

Tools

TensorFlow

JAX

Grain

tf.data service

Job description

Waymo is hiring for a senior ML infrastructure engineer to design and optimize distributed input data pipelines for large-scale ML training workloads. You will collaborate with researchers and ML engineers to remove bottlenecks and improve runtime goodput across distributed environments.

The role requires strong Python/C++ skills, experience with TensorFlow and JAX, and familiarity with modern ML infrastructure tools. This position is hybrid within the US.