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What You’ll Do
- 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
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What You’ll Bring
- Passion for your craft and demonstrated excellence in control systems engineering
- Extensive experience in designing, implementing, and deploying advanced control algorithms on real robotic system products (8+ years)
- Deep expertise in dynamics, kinematics, optimal control, and state estimation
- Strong command of hardware interfaces, sensors (IMUs, F/T sensors), and actuation technologies (motors, gearboxes, drivers)
- Production-level mastery of C++ with a track record of building reliable, safety-critical software
- Proven ability to bridge across hardware, software, and algorithms to deliver robust end-to-end systems
- An open mind about the power of deep learning, reinforcement learning, and vision-language-action models as critical for general-purpose robotics
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Bonus:
- Experience shipping humanoid robots or whole-body control systems
- Impactful published work in control theory, state estimation, or mathematical optimization
- Familiarity with parallel computation on GPUs to accelerate optimization
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Skills
Embedded control systems
Motion planning
Trajectory optimization
State estimation
Nonlinear MPC
Convex optimization
C++
Robotics
Dynamics
Kinematics
Sensor fusion
Hardware interfaces
Deep learning
Reinforcement learning
GPU computation
Location