Staff Robotic Controls Engineer, Humanoid AI

Apptronik; Austin (TX); On-site; USD 190,000 - 240,000; Full time

Job summary

Apptronik, a human-centered robotics company in Austin, TX, seeks a Staff Software Engineer – Humanoid Controls to elevate the controls stack for both wheeled-base and biped platforms. You will lead the translation of model-based and learning-based techniques into robust capabilities from simulation through hardware bring-up.

Ideal candidates have strong fundamentals in robotics, control theory, and real-time software, with hands-on experience in production-grade controllers and leadership to

Qualifications

• MS or PhD in Robotics, CS, EE, ME or related field (or equivalent experience).
• 8+ years of relevant industry experience in robot controls with track record on physical hardware.

Responsibilities

• Raise the quality of the overall controls stack — architecture, algorithms, real-time performance, testing, and long-term maintainability.
• Advance whole-body control across platforms — wheeled-base and biped — coordinate base with upper-body manipulation.
• Tackle high-impact problems — learning-based hand control, mobile manipulation, teleoperation, estimation.
• Translate state-of-the-art model-based and learning-based methods into robust capability from simulation to hardware bring-up and deployment.
• Expand performance envelope — speed, payload, reliability, and safety with measurable improvements.
• Set technical direction across the controls stack and align adjacent teams along the roadmap.
• Mentor engineers and multiply team capability through design and code reviews.

Skills

Robotics fundamentals

Control theory

Real-time software development

Mentoring/leadership

Education

MS or PhD in Robotics, CS, EE, ME

Tools

C++

Python

Job description

Apptronik, a human-centered robotics company in Austin, TX, seeks a Staff Software Engineer – Humanoid Controls to elevate the controls stack for both wheeled-base and biped platforms. You will lead the translation of model-based and learning-based techniques into robust capabilities from simulation through hardware bring-up.

Ideal candidates have strong fundamentals in robotics, control theory, and real-time software, with hands-on experience in production-grade controllers and leadership to