Senior Autonomy Software Engineer

Apptronik Apptronik · Robotics · HQ · Software Engineering

Senior Software Engineer to design and deploy learning-driven, mission-level autonomy systems for humanoid robots, enabling them to operate robustly in real-world human environments. Focuses on coordination, execution, and adaptation of robot behaviors using learning-based approaches, integrating outputs from various robot subsystems, and ensuring real-world deployment, robustness, and scalability.

What you'd actually do

  1. Design and implement mission-level autonomy systems for humanoid robots, focusing on learning-based decision making and behavior execution.
  2. Develop policy execution, monitoring, and coordination layers that integrate learning-based components with classical robot subsystems.
  3. Build autonomy frameworks that support adaptive behavior, generalization across tasks, and robustness to uncertainty and environmental variation.
  4. Implement recovery, fallback, and safety mechanisms around learning-based autonomy to ensure reliable real-world operation.
  5. Define and maintain clean interfaces between autonomy, perception, navigation, manipulation, and control systems.

Skills

Required

  • MS, or PhD in Robotics, Computer Science, Computer Engineering, or a related field.
  • 2+ years of experience developing robot autonomy or learning-based robotic systems.
  • Strong proficiency in modern C++ and working knowledge of Python in Linux environments.
  • Experience integrating learning-based policies (e.g., reinforcement learning, imitation learning, foundation-model-based policies) into real robot systems.
  • Solid understanding of robotics systems
  • Experience deploying autonomy software on physical robots, including debugging and tuning under real-world constraints.
  • Familiarity with ROS 2, message-passing architectures, and modular robot software design.
  • Strong software engineering fundamentals: testing, CI/CD, code reviews, documentation, and system reliability.

Nice to have

  • Experience with humanoid robots, mobile manipulators, or legged robotic systems.
  • Hands-on experience with reinforcement learning or learning-based control for robotics.
  • Familiarity with foundation models for robotics (e.g., vision-language-action models, multimodal policies).
  • Experience designing safe wrappers, monitors, or supervisors around learning-based systems.
  • Contributions to open-source robotics, autonomy, or ML infrastructure.
  • Experience working in fast-paced robotics startups or deploying systems into production.

What the JD emphasized

  • learning-based approaches
  • real-world deployment
  • robustness
  • scalability
  • learning-based decision making
  • learning-based components
  • learning-based autonomy
  • learning-based policies
  • learning-based control

Other signals

  • learning-driven autonomy
  • humanoid robots
  • real-world deployment
  • scalability