Foundation ML Engineer: Large-Scale Data & Models

Waymo; Mountain View (CA); On-site; USD 175,000 - 215,000; Full time

Benefits offered by this job

Discretionary annual bonus

Equity incentive program

Generous benefits

Job summary

Waymo is hiring for a role in its AI Foundations team focused on building and operating massive data systems and ML pipelines that drive the Waymo Driver. You will shepherd foundation models from prototype to production, and create automated benchmarking, monitoring, and deployment workflows.

The position emphasizes Python and C++ proficiency, experience with large-scale data infrastructure, and collaboration with AI/ML experts to translate research into real-world on-road improvements.

Qualifications

• Master's degree in Computer Science, Machine Learning, Robotics, or equivalent practical experience.
• Proficiency in Python and C++.
• Familiarity with PyTorch, JAX, or TensorFlow.
• Experience building/maintaining large-scale data pipelines or ML infrastructure.

Responsibilities

• Build and operate petabyte-scale data systems and ML pipelines.
• Shepherd foundation models from research to robust components in Waymo Driver.
• Create automated infrastructure for benchmarking, monitoring, and releasing models.
• Work with large-scale compute and frameworks to process data and train models.
• Improve end-to-end ML development lifecycle speed, reliability, and efficiency.
• Collaborate with cross-functional teams to translate model innovations into on-road improvements.

Skills

Python

C++

Frameworks (PyTorch, JAX, TensorFlow)

Large-scale ML pipelines

Education

Master's degree in CS/ML/Robotics

Tools

Flume

Spark

Borg

Kubeflow

Job description

Waymo is hiring for a role in its AI Foundations team focused on building and operating massive data systems and ML pipelines that drive the Waymo Driver. You will shepherd foundation models from prototype to production, and create automated benchmarking, monitoring, and deployment workflows.

The position emphasizes Python and C++ proficiency, experience with large-scale data infrastructure, and collaboration with AI/ML experts to translate research into real-world on-road improvements.