Senior ML Infrastructure Engineer - Hybrid

Waymo LLC; United States; Hybrid; USD 250,000 - 263,000; Full time

Benefits offered by this job

Discretionary annual bonus program

Equity incentive plan

Generous Company benefits program

Job summary

Waymo LLC is seeking an experienced machine learning/Software Engineer to design and optimize distributed data pipelines for large-scale training and inference. You will collaborate with researchers and ML engineers to resolve bottlenecks and improve data processing performance across multiple platforms.

The role emphasizes technical leadership in ML infrastructure, productionizing large models, and applying TensorFlow, JAX, ML Pathways, and Apache Beam to scalable systems.

Qualifications

• Bachelor's degree required and 5+ years of progressive experience in Software Engineering or related field.
• 5 years of experience in machine learning frameworks and distributed data processing libraries (TensorFlow, JAX, ML Pathways, Apache Beam).
• 2+ years of technical leadership in architecting ML infrastructure for large models (LLMs).

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 by optimizing input data processing for scalability and reliability.
• Implement and maintain ML infrastructure tools including ML Pathways, Grain, JAX, and TensorFlow.
• Evaluate and integrate modern technologies to enhance performance and scalability of ML systems.
• Promote best practices for distributed systems architecture and contribute to technical leadership.

Skills

TensorFlow

JAX

ML Pathways

Apache Beam

Python

C++

Distributed systems

Technical leadership

LLMs

Model serving

Education

Bachelor's degree in Computer Science, Mathematics, or related field

Job description

Waymo LLC is seeking an experienced machine learning/Software Engineer to design and optimize distributed data pipelines for large-scale training and inference. You will collaborate with researchers and ML engineers to resolve bottlenecks and improve data processing performance across multiple platforms.

The role emphasizes technical leadership in ML infrastructure, productionizing large models, and applying TensorFlow, JAX, ML Pathways, and Apache Beam to scalable systems.