Machine Learning Infrastructure Engineer

About the Role

This is an infrastructure engineering role at the core of building a large-scale physics foundation model — a novel class of AI designed to predict and influence physical systems. You'll sit at the intersection of ML systems engineering and cutting-edge research, directly enabling breakthroughs that go well beyond standard language or vision models.

What You'll Do

• Design, deploy, and maintain large distributed ML training and inference clusters.
• Build efficient, scalable end-to-end pipelines to manage petabyte-scale datasets across the full ML lifecycle.
• Research and implement parallelization techniques and numerical precision trade-offs at varying model scales.
• Profile and debug low-level GPU operations to squeeze out maximum performance.
• Stay current with the latest research and bring new ideas directly into production work.

What We're Looking For

• 2–10+ years of experience building ML infrastructure for core foundation model training (not just fine-tuning or deployment).
• Deep expertise optimizing large-scale training and inference workloads.
• Proficiency with distributed training frameworks such as FSDP or DeepSpeed.
• Hands-on experience across the ML lifecycle — data preparation, training, evaluation, and optimization.
• Background working in science or physical AI domains (e.g., autonomous vehicles, robotics, computational biology, or similar).
• Familiarity with cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings.
• Experience with containerization and orchestration tools such as Kubernetes and Docker.
• Knowledge of monitoring, logging, observability, and version control best practices for ML systems.
• Low-level GPU performance optimization experience (CUDA, JAX) is a strong plus.
• Comfort thriving in a fast-paced, demanding engineering culture.

Compensation

& Benefits

Salary range: $200,000 – $400,000 USD annually. Visa sponsorship is not available.

Location

On-site, 5 days per week in San Francisco, CA.