Genesis AI - Staff Software Engineer, Controls

Staff Software Engineer, Controls

Software Engineering, Artificial Intelligence (AI), Artificial Intelligence (AI), Robotics, Embedded Systems, Optimization Algorithm, Problem Solving Skills, Hardware Simulation, Debugging Skills, Control Systems, * 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
• Bonus: Experience shipping humanoid robots or whole-body control systems
• Bonus: Impactful published work in control theory, state estimation, or mathematical optimization
• Bonus: Familiarity with parallel computation on GPUs to accelerate optimization

Genesis is a global physical AI lab and full-stack robotics company. We build generalist robots to unlock unlimited physical labor, allowing humans to focus on creativity, curiosity, and what they love. We recently raised a $105M Seed round backed by Khosla Ventures and Eclipse, and we’re looking to expand our software and hardware teams rapidly. 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