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…
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…
General-purpose embodied AI and robotics: enabling robots to understand language and vision and translate that into physical actions across novel tasks.
Founded
2024
Industry
Research Services
Funding Track Record
- 2024
$470M
2024 figure listed in a Tracxn report.
$400M
Reported $400M financing round (headline coverage) valuing the company at about $2B.
- 2026
Just over $1B (cumulative)
Reported cumulative funding and that the company was in talks to raise an additional ~$1B.
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Who you are
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
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
What the job involves
Startup jobs. A lot of them.
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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 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
Manage compute resources: Ensure efficient allocation and utilization of cloud-based GPU/TPU compute while controlling cost
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
