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How much do software engineer machine learning infrastructure jobs pay per year?

As of Sep 9, 2026, the average yearly pay for software engineer machine learning infrastructure in the United States is $180,266.00, according to ZipRecruiter salary data. Most workers in this role earn between $173,000.00 and $205,000.00 per year, depending on experience, location, and employer.

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For Software Engineer Machine Learning Infrastructure jobs, the most frequently searched job titles are:

Senior Machine Learning Infrastructure Engineer, Simulation

San Diego, CA • On-site

Waymo
Internet and IT • 1 - 5K employees
Learn more about Waymoexternal

$115K - $156K/yr

Full-time

Re-posted 8 days ago

Key responsibilities

  • Design and scale large distributed systems supporting the ML lifecycle, including dataset generation and model training.

  • Collaborate with cross-functional teams to derive system requirements and translate product goals into technical deliverables.

  • Own and lead the development of advanced AI/ML infrastructure for large-scale foundation models in simulation environments.

Job description

The Simulation ML Infrastructure team builds scalable AI/ML infrastructure to accelerate the Simulator team in sustainably innovating and building state of the art simulations of realistic environments for the testing and training of the Waymo Driver. To increase the fidelity and steerability of the simulations, we employ large foundation models trained on massive datasets to model the real world, including but not limited to, realistic agents (vehicles, pedestrians, cyclists, motorcyclists etc.), roads, traffic control systems, and weather etc.

We seek an experienced Senior Machine Learning Infrastructure Engineer to lead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models in ML accelerator-friendly simulations. Your expertise in massive model scaling, ML accelerators, and distributed training will be required for designing and scaling our systems.

This role reports to an Engineering Manager.

You will:

  • Be part of a world-class, high-performing research engineering team to advance the state of the art of ultra realistic multi-agent simulations using foundation models.

  • Collaborate closely with the core Google DeepMind and Waymo Realism Modeling teams in London, and Waymo Oxford to use the large models to improve sim realism.

  • Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models. Work at the intersection of data engineering, model development, and deployment, and provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.

  • Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation and model training.

  • Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.

  • Mentor junior engineers, growing their expertise and fostering a collaborative culture.

You have:

  • BS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience

  • 5+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

We prefer:

  • 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

  • Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, or similar frameworks.

  • Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.

  • Deep understanding of state-of-the-art machine learning models such as auto-regressive transformers and familiarity with custom-kernels for diverse h/w compute based efficiency.

  • Excellent communication skills, both verbal and written, with the ability to translate complex technical concepts for a broad audience.

  • Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a plus.

Apply