ML Infra Engineer, Modeling

Physical Intelligence; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Physical Intelligence in San Francisco is seeking a hands-on ML infrastructure engineer to scale and optimize our training systems. You will own large-scale training infra, manage GPU/TPU compute, job orchestration, checkpointing, and memory profiling, building efficient JAX pipelines that turn research ideas into production runs.

You’ll work with researchers and model engineers to push the limits of scalable ML, contribute to core training code, and help ensure reliability and speed across

Qualifications

• Strong software engineering fundamentals and ML infra experience.
• Hands-on large-scale training experience in JAX or PyTorch.
• Experience with distributed training and multi-host setups.
• Experience deploying training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS).
• Ability to debug and optimize performance bottlenecks in the training stack.

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.
• 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.

Skills

Software engineering fundamentals

Large-scale ML training

JAX

PyTorch

Distributed training

Cloud platforms

Performance debugging

Cross-functional collaboration

Tools

SLURM

Kubernetes

GCP TPU/GKE

AWS

Job description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.

In this role you will help scale and optimize our training systems and core model code. You'll own critical infrastructure for large-scale training, from managing GPU/TPU compute and job orchestration to building reusable and efficient JAX training pipelines. 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.

The Team

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.

In This Role You Will

• 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.

What We Hope You’ll Bring

• 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.

Bonus Points If You Have

• 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.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.