This is a hands-on, high-leverage role at the intersection of ML, software engineering, and scalable infrastructure.

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.

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.

ML Infra Engineer, Modeling

Responsibilities

Qualification

Required

Preferred

Company

Physicalintelligence

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