ML Infra Engineer, Modeling
San FranciscoFull-timePosted 1mo ago
About the role
Role focused on scaling and optimizing training systems and core model code for large-scale, production-grade ML training infrastructure, with emphasis on JAX-based pipelines, TPU/GPU clusters, and collaboration with researchers.
Responsibilities
- Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and metrics/logging.
- Scale distributed training: Work with researchers to scale JAX-based training across TPU and GPU clusters with minimal friction.
- Optimize performance: Profile and improve memory usage, device utilization, throughput, and distributed synchronization.
- Enable rapid iteration: Build abstractions for launching, monitoring, debugging, and reproducing experiments.
- Partner with researchers: Translate research needs into infra capabilities and guide best practices for training at scale.
- Contribute to core training code: Evolve JAX model and training code to support new architectures, modalities, and evaluation metrics.
Qualifications
- Strong software engineering fundamentals and experience building ML training infrastructure or internal platforms.
- Hands-on large-scale training experience in JAX (preferred), PyTorch.
- Familiarity with distributed training, multi-host setups, data loaders, and evaluation pipelines.
- Experience managing training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS).
- Ability to debug and optimize performance bottlenecks across the training stack.
- Strong cross-functional communication and ownership mindset.
Skills
Required:Software engineeringJAXDistributed trainingCloud platformsDebugging/performance optimization
Nice to have:PyTorch
Last updated Sep 2, 2026. Interested? Apply on the company site.
About Physical Intelligence
Physical Intelligence (π)
Physical Intelligence is bringing general-purpose AI into the physical world.
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