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Lead customer robot deployments from workflow discovery and site assessment through scoping, financial modeling, technical alignment, execution, acceptance testing, training, and handoff. Partner with engineering, research, safety, solution engineering, customers, and external teams to define requirements, success metrics, dependencies, and milestones. Translate field feedback into deployment practices while managing scope changes, implementation tradeoffs, customer value, utilization, and support needs.

Owns the technical path from customer use cases to reliable robot deployments. Responsibilities include assessing automation feasibility, defining requirements and success metrics, designing integrations across robotics hardware, software, tooling, and customer systems, configuring and debugging systems onsite, and improving deployment tools and playbooks. The role partners with research and engineering teams to resolve field failures and requires customer communication, hands-on troubleshooting, and travel to deployment sites.

Build and manage a production supplier base for robotic hardware, including sourcing, qualification, dual sourcing, commercial negotiations, capacity planning, BOM and cost control, supplier quality, logistics, and MRP systems. Ensure clear-to-build readiness, inventory accuracy, supplier performance, and production support while developing domestic and international suppliers and leading cross-functional collaboration with engineering, manufacturing, quality, legal, and finance.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Leads complex technical programs spanning AI research, robotics hardware, software, simulation, and foundation model training. Builds roadmaps, aligns Bay Area and Paris teams, manages dependencies and risks, establishes execution metrics, and automates program management workflows. Requires technical fluency across machine learning and hardware integration, strong communication, rapid domain learning, decisive execution, and ownership of outcomes.

Own the deployment-focused roadmap for Physical AI products, translating robotics research into reliable customer-facing solutions. Define release readiness criteria, success metrics, reliability thresholds, and scope. Coordinate research, robotics engineering, and field operations while supervising customer and partner pilots. Collaborate on robot, data collection, privacy, and infrastructure requirements, making product decisions amid technical ambiguity and incomplete information.

Build scalable infrastructure for robotics simulation, including workload management, environment frameworks, task interfaces, evaluation tooling, and real-to-sim pipelines. Collaborate with policy training and asset-generation teams to improve sim-to-real performance. The role requires extensive physics simulation expertise, particularly rigid body simulation or FEM, along with CUDA and GPU programming proficiency. Experience with robotics simulation platforms, synthetic data, asset pipelines, manipulation, humanoids, or embodied AI is advantageous.

Operate robots through teleoperation equipment during onsite data collection sessions. Follow predefined tasks and standard operating procedures, monitor robot behavior, identify unsafe or unexpected activity, and maintain safety and consistency during repetitive sessions. Provide session feedback, remain focused for extended periods, and collaborate with operations staff and session leads. The role is hands-on and execution-focused, with technical setup and hardware maintenance handled by other teams.

Build and optimize distributed foundation-model training infrastructure across multi-node GPU clusters. Profile data pipelines, GPU kernels, memory, networking, and CPU/GPU workloads to improve convergence speed and utilization. Develop CUDA, cuDNN, Triton, and custom kernel solutions integrated with PyTorch. Create monitoring and debugging tools for large-scale training runs and diagnose performance issues.

Build and maintain the software engineering backbone of Genesis-World, an open-source robotics simulation platform. Responsibilities include scalable testing for JIT-compiled engines, deterministic reproduction and serialization, autodiff infrastructure, scene editing, extensible plugins, robot actuation, event systems, timing architecture, refactoring, performance optimization, and cross-platform debugging. The role also involves code review, issue triage, contributor support, and collaboration with compiler and physics teams.