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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.
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