Jobs at Physical Intelligence

This position offers internship opportunities for individuals interested in gaining research experience.

Lead end-to-end NPI program execution for robotic hardware platforms: schedule and risk management, material forecasting, CTB analysis, build planning, vendor coordination, logistics readiness, configuration management, and cross-functional handoffs to fleet integration to support research and deployments.

As an ML Infra Engineer, you will optimize and manage training systems for large-scale models, collaborating with researchers and engineers on infrastructure and experiment scalability.

The Build & Release Engineer will manage infrastructure and developer platforms, focusing on CI/CD systems, build reliability, and Kubernetes services to improve developer productivity and operational efficiency.

Looking for researchers in machine learning and robotics with strong implementation and system-building skills. Open to diverse backgrounds.

Design and rapidly fabricate mechanical, electrical, and sensor prototypes for robotics research. Collaborate with researchers and engineers to translate experiments into CAD, PCBs, wiring, sensor/actuator integration, and validated end-to-end prototype systems. Build fixtures, test rigs, and tooling; perform hands-on assembly, debugging, and documentation to support real-world deployments.

Build and own the manufacturing and hardware-test software platform: convert procedures into repeatable workflows, implement local station software, integrate inventory and firmware management, ensure observability and resilience, automate tests, and debug across cloud, Linux stations, embedded devices, and test equipment.

Lead sourcing and supplier execution for the full robot BOM, build resilient global supply base, drive cost and should-cost improvements, manage CM transitions and NPI ramps, and ensure material continuity using MRP/ERP systems while partnering with engineering and executive leadership on supply risk and capacity planning.

The role involves integrating robot platforms into a fleet, defining and executing acceptance criteria, maintaining performance, and collaborating cross-functionally to improve system reliability.

This role involves executing cross-functional projects, driving partnerships, managing deployment efforts, and supporting communications with investors and external parties.

Design and implement control algorithms for robotics, develop real-time control loops, and debug complex hardware/software issues while collaborating with cross-functional teams.

As a Forward Deployed Robotics Engineer, you'll manage robotic systems' technical issues in real-world settings, performing diagnostics, collaborating with teams, and analyzing data for resolution and improvements.

As an ML Infra Engineer, you will optimize and scale training systems, manage GPU/TPU compute, and develop JAX training pipelines, collaborating with researchers for efficient model training.

Design and implement low-latency teleoperation interfaces and real-time video/streaming systems for on-site robots. Profile and optimize across drivers, networking, compute, and storage; build reliability, tracing, and debugging tooling; collaborate with researchers, hardware engineers, and operators to deploy dependable production robotics systems.

Deploy, debug, and iterate learned policies on physical robots across perception, policy, control, and hardware. Train and tune policies, curate data, build production-quality code, set up tests and data pipelines with operators, engage partners in deployment contexts, and translate research advances into reliable, real-world robotic systems.

Engineer low-latency, high-throughput sensor and actuator pipelines, optimize system performance, and integrate complex models into real-time workflows while collaborating across teams.

Full-lifecycle technical recruiter owning searches for ML, robotics, systems, and infrastructure roles. Source passive top-tier candidates, design interview processes, advise on compensation and closing strategies, partner with founders and hiring managers, build market maps and dashboards, and steward candidate experience with data-driven sourcing and hiring approaches.

Build and operate large-scale data infrastructure for robot learning: ingest, validate, transform, and featurize multimodal data; run batch and streaming workflows; design storage and metadata systems; optimize training-time performance; manage data movement and lifecycle; implement observability and guardrails; collaborate with researchers and engineers.

Lead and scale data annotation operations from hundreds to thousands, owning throughput, quality, cost, hiring, training, tooling, vendor mix, and budget. Build multi-layer management, human-in-the-loop/autolabeling workflows, QA/rubrics, operational metrics, capacity planning, and partner with product, engineering, and research to meet SLAs and improve unit economics.

Own product roadmap for Research Operations; design and improve systems for intake, prioritization, resource allocation, execution visibility, quality management, and operational planning. Partner with engineers, project leads, Production Operations, and researchers to build tools, standardize processes, establish metrics and dashboards, remove bottlenecks, and prioritize investments that scale research execution.