P
Physical Intelligence
Machine Learning Infrastructure Engineer (Modeling)
Skills
Google Cloud PlatformPyTorchMachine LearningAWSKubernetes
2 months ago
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Important information
Contract type:
Permanent
Location:
San Francisco, CA, USA
Work mode:
Onsite
Published on:
4 September 2026
What they need
- The ML Infrastructure team supports and accelerates PI’s core modeling efforts by building the systems that make large-scale training reliable, reproducible, and fast. The team works closely with research, data, and platform engineers to ensure models can scale from prototype to production-grade training runs
- 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- Strong software engineering fundamentals and experience building ML training infrastructure or internal platforms
- Strong cross-functional communication and ownership mindset
- Experience managing training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS)
- Familiarity with distributed training, multi-host setups, data loaders, and evaluation pipelines
- Ability to debug and optimize performance bottlenecks across the training stack
- Hands-on large-scale training experience in JAX (preferred), PyTorch
- Experience designing abstractions that balance researcher flexibility with system reliability
- Background in robotics, multimodal models, or large-scale foundation models
- Experience operating close to hardware (GPU/TPU performance tuning)
- Deep ML systems background (e.g., training compilers, runtime optimization, custom kernels)
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