Staff Software Engineer

Who you are

• Deep robotics fundamentals: kinematics, dynamics, state estimation, and optimization/control theory
• Proven expertise in whole-body control (WBC/OSC, QP-based control, or MPC) for floating-base (legged) or mobile (wheeled) systems
• Hands-on experience with learning-based control / reinforcement learning applied to robotics (training in simulation)
• Breadth across robot control problems — mobile manipulation, dexterity / hand control, and teleoperation — and the versatility to move between them
• Strong software engineering: real-time C++ and Python for robotic systems
• Industry experience developing production-grade controllers, including hands-on experience validating or deploying algorithms on physical robot hardware
• Preferred: shipping controllers to a production fleet or to market at scale; dexterous / in-hand manipulation; legged locomotion; or safety-critical / certified software (ISO 13849, 10218, IEC 61508)
• MS or PhD in Robotics, CS, EE, ME, or a related field (or equivalent experience)
• 8+ years of relevant industry experience (or 4+ with a PhD) in robot controls, with a track record of getting controllers working on physical hardware
• Prolonged periods of sitting at a desk and working on a computer
• Must be able to lift 15 pounds at times
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate

What the job involves

• Apptronik is building robots for the real world — to improve human quality of life and help solve the ever-increasing labor shortage
• As a Staff Software Engineer – Humanoid Controls, you will be a senior technical force multiplier on our Motion Control & Planning team — the person who raises the quality of our robotics stack and moves to wherever the hardest problem is
• Your scope spans whole-body control for our wheeled-base platform and our biped, learning-based hand control, mobile manipulation, and teleoperation — translating state-of-the-art controls and learning into real capability while setting technical direction for the team
• Raise the quality of the overall controls stack — architecture, algorithms, real-time performance, testing, and long-term maintainability
• Advance whole-body control across our platforms — the wheeled-base robot and the biped — coordinating the base or legs with upper-body manipulation
• Flex across the stack to the highest-impact problems — e.g. learning-based hand control (RL in simulation), mobile manipulation, teleoperation, and estimation
• Translate state-of-the-art model-based and learning-based methods into robust capability, from simulation through hardware bring-up and deployment
• Expand the robot's performance envelope — speed, payload, reliability, and safety — with measurable, defensible improvements
• Set technical direction across the controls stack, influencing the roadmap and bringing adjacent teams (locomotion, manipulation, teleoperation, RL, estimation) along
• Mentor engineers and multiply team capability through design reviews, code reviews, and best practices
• Collaborate cross-functionally with hardware, systems, and RL teams to co-develop solutions and diagnose system-level issues