Senior ML Systems Engineer - Distributed Evaluation & Efficiency

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

Benefits offered by this job

Bonus program

Equity incentives

Benefits package

Job summary

Waymo is seeking engineers with ML system expertise to train, evaluate, and improve pre-trained models for the Waymo Driver, and to build scalable evaluation platforms across compute, data, and environments.

You will work with researchers and modeling engineers to tackle evaluation challenges, optimize performance, and advance ML infrastructure with TensorFlow, JAX, XLA, and related tools at scale.

Qualifications

• B.S. in Computer Science, Math, or 3+ years equivalent real-world experience.
• Proficient in distributed systems design with an understanding of ML efficiency.
• Experience with ML frameworks, including TensorFlow, JAX, XLA.
• Solid programming skills in Python and C++.
• Practical familiarity with profiling tools to uncover performance bottlenecks.

Responsibilities

• Design and build distributed evaluation platforms for large-scale ML evaluation workloads.
• Profile evaluation platforms, identify performance bottlenecks, and implement optimizations to improve inference speed and resource utilization.
• Collaborate with ML engineers to understand evaluation requirements and scenarios, and improve DevX of the evaluation infrastructure.
• Improve runtime goodput of ML inference workload and efficiency of metrics computations, ensuring scalability and reliability across distributed environments.
• Implement and maintain advanced ML infrastructure tools, including ML Pathways, JAX, Flume and TensorFlow.

Skills

Distributed systems

ML frameworks (TensorFlow/JAX/XLA)

Python

C++

Profiling tools

Education

BS in Computer Science

MS in Computer Science / Math

Tools

ML Pathways

JAX

Flume

TensorFlow

XLA

Job description

Waymo is seeking engineers with ML system expertise to train, evaluate, and improve pre-trained models for the Waymo Driver, and to build scalable evaluation platforms across compute, data, and environments.

You will work with researchers and modeling engineers to tackle evaluation challenges, optimize performance, and advance ML infrastructure with TensorFlow, JAX, XLA, and related tools at scale.