Jobs at Physical Intelligence

Secure models, data, cloud infrastructure, and compute by building practical security systems and automation. Partner with engineering and research teams on secure architecture and development workflows, manage access controls and infrastructure safeguards, assess and exploit vulnerabilities, validate fixes, and contribute production-quality tools. The role also establishes security priorities and may secure AI/ML infrastructure and agent execution environments.

Owns training and readiness programs for a robot operator workforce, including candidate work trials, onboarding, ramp milestones, workflow training, trainer coordination, certifications, and continuous improvement. The role translates technical process changes into simple, repeatable training, tracks readiness and performance, identifies training gaps, and improves time-to-productivity across teams, shifts, and locations.

Own full-cycle recruiting for hardware roles spanning mechanical, electrical, firmware, controls, manufacturing, NPI, testing, supply chain, and technicians. Partner with founders and technical leaders to define talent strategy, source specialized candidates, design interview processes, close senior hires, and build technician and factory talent pipelines. The role requires strong hardware market knowledge, analytical rigor, bespoke closing strategies, and regular presence at the San Francisco office and Fremont factory.

Maintains physical research sets by auditing organization and cleanliness, staging props, logging inventory, identifying discrepancies, and delivering kits to operators. Follows checklists, escalates missing or out-of-spec items, and supports uninterrupted robot research operations. Requires attention to detail, reliability, basic inventory tracking experience, and the ability to stand, move, and lift up to 40 pounds throughout a shift.

Own and scale AI-native platform infrastructure, including Kubernetes clusters, microservices, multi-cloud foundations, networking, authentication, observability, security, cost optimization, and developer tooling. Improve reliability through safe deployments, autoscaling, SLOs, incident response, and standardized infrastructure patterns. Collaborate with researchers and engineers to design, operate, and evolve scalable systems for machine learning, simulation, and robotics workloads.

Build and operate a scalable model API platform for data ingestion, fine-tuning, evaluation, low-latency inference, and deployment. Design reliable multi-tenant infrastructure with rate limiting, isolation, observability, versioning, and SLOs. Collaborate with researchers and external partners to turn evolving AI capabilities into intuitive developer tools, APIs, and documentation. The role emphasizes production software engineering, backend systems, infrastructure, and model serving rather than robotics research or customer success.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

Owns end-to-end performance for a portfolio of AI annotation programs, partnering with researchers to define requirements, rubrics, quality standards, and delivery operations. Drives continuous improvement, scalable solutions, and operational efficiency while coordinating technical and nontechnical stakeholders, vendors, and remote teams. Requires strong systems thinking, communication, organization, and understanding of model training and evaluation. Experience with ML annotations, robotics, multimodal data, or coding agents is advantageous.

Build internal fullstack products and human-robot interfaces for operating, monitoring, debugging, and deploying robots. Responsibilities include realtime video and robot-state systems, safety-focused UX, on-device and cloud architecture, fleet versioning, observability, workflow measurement, and direct user-driven product development. The role partners with researchers, operators, and roboticists across lab, warehouse, and field environments.

Design, prototype, test, and integrate robotic arm and mobile platform subsystems spanning mechanical, electrical, embedded, and software disciplines. Responsibilities include hardware bring-up, assembly, calibration, reliability testing, data-driven debugging, and root-cause analysis. The intern will own subsystem development from CAD and PCB work through bench testing while supporting robotics data collection and model training operations.

Own high-volume recruiting for Robot Operators and related site-based roles in San Francisco. Build candidate pipelines through job boards, referrals, partnerships, events, schools, and community organizations; conduct efficient initial screens; manage funnel throughput and candidate experience; track conversion metrics; partner with operations leaders; and improve repeatable recruiting processes and automation.

Design and implement embedded firmware for data-collection devices, including embedded Linux bring-up, sensor drivers, BSP customization, kernel debugging, hardware/software integration, performance tuning, and power optimization. Collaborate with research, hardware, software, and fleet teams to develop first-of-their-kind devices, debug complex concurrency and interface issues, and bring products from concept to production.

Build and deploy internal AI tools, agents, integrations, automation, and workflows that improve productivity across engineering, research, operations, and recruiting. Drive adoption through enablement, documentation, training, and best practices. Evaluate AI tools, measure impact, and ensure secure data access and permissions while partnering across technical and business teams.

Own and optimize infrastructure for large-scale ML training and inference. Build JAX-based pipelines, manage GPU and TPU workloads, improve distributed performance, and create systems for scheduling, monitoring, checkpointing, debugging, and reproducible experimentation. Partner with researchers to translate modeling ideas into production training runs and contribute to core training code supporting new architectures, modalities, and evaluation metrics.

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.

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.

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.

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