Genesis AI - Staff Software Engineer, Controls
OpenReq, San Carlos, CA, United States
Staff Software Engineer, Controls
Company
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
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
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
Company
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
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
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