Physical Intelligence
ML Infra Engineer, Modeling
San Francisco
Sponsorship not specifiedDetected 46 days ago
AlgorithmsAWSGCPCloud PlatformsKubernetesMachine LearningRoboticsResearchCommunication
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Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- Physical Intelligence is bringing general-purpose AI into the physical world.
- You'll work closely with researchers and model engineers to translate ideas into experiments-and those experiments into production training runs.
- This is a hands-on, high-leverage role at the intersection of ML, software engineering, and scalable infrastructure.
Responsibilities
- Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and metrics/logging.
- 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.
- Ability to debug and optimize performance bottlenecks across the training stack.
Nice to have
- 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).
- Strong cross-functional communication and ownership mindset.
- Deep ML systems background (e.g., training compilers, runtime optimization, custom kernels).
- Experience operating close to hardware (GPU/TPU performance tuning).
- Background in robotics, multimodal models, or large-scale foundation models.
- Experience designing abstractions that balance researcher flexibility with system reliability.
Benefits
- Bonus Points If You Have
Company info
- 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.
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