Design Verification, Forward Deployed Engineering
Serve as the design verification SME for semiconductor deployments, helping teams reason about verification workflows across block, subsystem, and SoC environments. Shape AI-assisted workflows for test generation, regression triage, debug, root-cause analysis, and coverage closure. Curate evaluations with FDEs and customer SMEs, including golden tasks, labeled examples, rubrics, acceptance criteria, and realistic benchmarks grounded in solved issues and real engineering workflows. Build lightweight prototypes, evaluation harnesses, and tooling that validate opportunities and improve solution quality. Educate and mentor the broader FDE team on verification concepts, tooling, and methodology so the organization can engage semiconductor workflows with greater depth and confidence. Partner with FDEs during customer discovery and scoping to translate ambiguous pain points into clear solution hypotheses, success criteria, and technical plans. Support customer-facing technical conversations as a trusted advisor, engaging credibly with technical leaders. Progressively take on broader Forward Deployed Engineering responsibilities, including customer discovery, solution architecture, prototype development, production deployment, and ownership of technical workstreams.
Robotics Autonomy Engineer-Planning and Control (Federal)
Design, implement, and deploy advanced motion planning and control algorithms for robotic platforms including wheeled, legged, and humanoid systems. Develop robust motion planning algorithms for challenging real-world scenarios such as narrow passages, dynamic obstacles, and complex environments. Design optimization-driven approaches for path and trajectory generation to ensure smooth, reliable, and efficient navigation. Ensure scalability, reusability, and adaptability of planning approaches across diverse contexts. Develop and tune control algorithms for precise trajectory tracking and stable operation. Collaborate to coordinate perception, planning, and control layers for robust performance. Build and maintain testing pipelines from unit validation to full robot deployment. Utilize simulation and testing environments for evaluation, benchmarking, and regression validation. Analyze real-world telemetry for diagnosis, improvement, and robustness enhancement. Investigate and resolve field deployment issues through data analysis and debugging. Deliver improvements addressing specific challenges while maintaining general reliability and performance.
Lead Software Engineer, Advanced Pilot Assistant Software (Autonomy/Robotics)
Design, build, and deploy robotic and embedded software that powers advanced pilot assistance systems in production environments. Own autonomy-related features or subsystems from concept through deployment, focusing on reliability and performance. Write, review, and maintain high-quality Python and C++ code across autonomy, systems, and embedded components. Integrate software with hardware, sensors, and perception or data ingestion pipelines to support autonomous and operator-in-the-loop decision-making. Optimize software for edge compute environments by managing CPU/GPU usage, latency, and implementing safety mechanisms and fail-safes. Lead testing, validation, and deployment efforts to ensure systems meet safety-critical and mission-critical requirements. Mentor engineers and contribute to technical direction through design reviews, code reviews, and hands-on collaboration.
Member of Technical Staff, Simulation (Bay Area, Remote)
Develop a high-throughput, GPU-based simulation pipeline primarily for rigid body simulation for robots to train robotics foundation models. Implement essential robotics features, including actuators, sensors, and controllers, in collaboration with the robotics team. Work with the policy training team to bridge the sim-to-real gap. Develop a real-to-sim pipeline in collaboration with the asset generation team. Collaborate with a team committed to building general-purpose Physical AI.
Member of Technical Staff, Simulation (Paris, London)
Develop a high-throughput, GPU-based simulation pipeline primarily for rigid body simulation for robots to train robotics foundation models. Implement essential robotics features, including actuators, sensors, and controllers, in collaboration with the robotics team. Work with the policy training team to bridge the sim-to-real gap. Develop a real-to-sim pipeline in collaboration with the asset generation team. Collaborate with a team committed to building general-purpose Physical AI.
Member of Technical Staff, Robot Learning (Paris, London)
Develop and optimize a learning-based robotic manipulation control stack. Design and maintain a teleoperation system with smooth, precise motion and low latency. Train robotic policies for manipulation and locomotion with reinforcement learning and imitation learning. Deploy robotic policies and diagnose latency or bottlenecks in the control pipeline. Analyze and minimize the sim-to-real gap by co-optimizing simulation and real-world robot behavior. Collaborate with a team of driven individuals committed to building general-purpose Physical AI.
Member of Technical Staff, Robotics (Bay Area)
Design, implement, and optimize the embedded control stack for general-purpose robots. Design motion planning and trajectory optimization algorithms for dynamic locomotion and manipulation. Build real-time state estimation pipelines for pose, contact, and force sensing, fusing heterogeneous sensor data under noise and uncertainty. Formulate and solve optimal control problems (nonlinear MPC, convex optimization, trajectory optimization) for high-performance and stable behavior. Build modular and robust software frameworks enabling rapid iteration between simulation and hardware. Lead debugging, tuning, and validation of controllers directly on physical robots.
Member of Technical Staff, Robotics (Paris, London)
Design, implement, and optimize the embedded control stack for general-purpose robots. Design motion planning and trajectory optimization algorithms for dynamic locomotion and manipulation. Build real-time state estimation pipelines for pose, contact, and force sensing by fusing heterogeneous sensor data under noise and uncertainty. Formulate and solve optimal control problems including nonlinear MPC, convex optimization, and trajectory optimization for high-performance and stable behavior. Build modular and robust software frameworks enabling rapid iteration between simulation and hardware. Lead debugging, tuning, and validation of controllers directly on physical robots.
Member of Technical Staff, Rendering (Bay Area, Remote)
Develop a high-throughput rendering pipeline for training robotics foundation models. Design protocols and interfaces between the rendering pipeline, physics engine, and 3D generative models. Build an efficient platform for large-scale robotics training and evaluation. Build a closed-loop system to minimize the sim-to-real gap in rendering for robotics policy training. Collaborate with a team of driven individuals committed to building general-purpose Physical AI.
Member of Technical Staff, Rendering (Paris, London)
Develop a high-throughput rendering pipeline for training robotics foundation models. Design protocols and interfaces between the rendering pipeline, physics engine, and 3D generative models. Build an efficient platform for large-scale robotics training and evaluation. Build a closed-loop system to minimize the sim-to-real gap in rendering for robotics policy training. Collaborate with a team of driven individuals committed to building general-purpose Physical AI.
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