ML Infra Engineer, Modeling

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

Job summary

Physical Intelligence is seeking an hands-on ML Infrastructure Engineer to scale and optimize our training systems and core model code. You will own critical infrastructure for large-scale training, managing GPU/TPU compute, job orchestration, and building efficient JAX training pipelines.

You’ll collaborate with researchers and model engineers to translate ideas into experiments and production runs, contributing to core training code for new architectures and multimodal models.

Qualifications

• Hands-on experience building ML training infrastructure.
• Proficiency with JAX and PyTorch.
• Familiarity with distributed training and multi-host setups.
• Experience with cloud platforms (GCP, AWS) and orchestration tools.

Responsibilities

• Own training/inference infrastructure including scheduling, checkpointing, and metrics.
• Scale distributed training across TPU and GPU clusters.
• Profile and optimize memory, throughput, and synchronization.
• Build abstractions for launching, monitoring, and reproducing experiments.
• Collaborate with researchers to translate ideas into production training runs.

Skills

Software engineering

Large-scale training

JAX

PyTorch

Distributed training

Cloud platforms

Tools

JAX

PyTorch

Kubernetes

SLURM

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.