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Apptronik

Develops AI-powered humanoid robots for manufacturing

Controls Engineer – Actuation - Humanoid Sim-to-Real

Controls & Automation EngineeringConfirmed live in the last 24 hours
Full-timeAustin, TX, USANo salary listedIn-person

About the Job

Requirements

  • Strong C++ skills, including extending a simulator or physics-engine codebase rather than only consuming it through a Python interface.
  • Deep grounding in motor electromechanics and classical control, including closed-loop torque/current control, controller design and tuning, and frequency-domain analysis such as Bode plots and stability margins.
  • Hands-on actuator or servo control experience on real hardware, including designing, tuning, and validating low-level controllers informed by system identification.
  • Hands-on system identification and modeling experience, including structured experiment design and fitting models from real data for simulation and control.
  • Proficiency in Python for test automation, data analysis, and tooling, and a track record of shipping maintainable code under Git in a fast-moving R&D environment.
  • Comfort working cross-functionally with controls engineers and focusing on how models and controllers are used, not only whether they fit data.
  • A BS in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field and four years of relevant experience; an MS in a related field and two years of relevant experience; or a PhD in a related field.

Responsibilities

  • Develop and maintain high-fidelity actuator models in simulation that capture physical behaviors such as friction, compliance, thermal effects, and torque and current limits affecting real-world performance.
  • Design, tune, and deploy actuator-level control, including closed-loop torque and current control and low-level control that translates commands into reliable joint behavior on hardware.
  • Design and run hardware test campaigns to characterize actuator behavior and extract parameters that keep models and controllers accurate.
  • Apply system identification to extract physical models from experimental data and use those models to inform controller design and gain selection.
  • Quantitatively validate sim-to-real fidelity for models and controllers using appropriate comparison metrics, and understand the limitations of each.
  • Build and maintain robust Python and C++ tooling for experiment automation, model extraction, controller tuning, and simulator integration.
  • Partner with the Controls team to align on required model fidelity and control performance, understand how model and controller choices affect training and policy performance, and identify when the actuator layer is the root cause of a sim-to-real gap.
  • Support hardware bring-up and deployment by translating validated models and controllers into real robot configuration.

Desired Qualifications

  • Experience with dynamometers, motor test stands, and lab instrumentation.
  • Experience with motor thermal modeling and gearbox efficiency or backlash characterization.
  • Experience with real-time actuator communication protocols and embedded or firmware control.
  • Experience with a modern robotics simulator such as MuJoCo, Isaac Sim/Lab, or Brax, and sim-to-real validation workflows.
  • Experience with Docker and GPU/cloud experiment infrastructure.
  • Prior humanoid or legged-robot experience.

About the Company

Apptronik

Develops AI-powered humanoid robots for manufacturing

Apptronik builds Apollo humanoid robots powered by AI to work with people in manufacturing, logistics, and planned expansion to retail, healthcare, and home. Apollo robots perform physical tasks such as transporting components, sorting, and kitting, working alongside human workers. They differentiate themselves through a human-centered approach linked to the UT Austin robotics lineage and NASA’s Valkyrie, plus partnerships with Mercedes-Benz, GXO Logistics, Jabil, and Google DeepMind Gemini Robotics, with strong funding to scale production and training. Their goal is to scale Apollo deployments to enable safer, more efficient collaboration between humans and robots across multiple sectors.

Company size
501-1,000
Company stage
Private
Total funding
$949.8M
Headquarters
Austin, TX
Founded
2015

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