Apptronik

Lead the end-to-end development of learned manipulation systems for Apollo humanoid robots, from data strategy and model training through simulation, hardware transfer, and production deployment. This principal-level role combines robotics, computer vision, and machine learning to enable reliable real-world manipulation.

Own the architecture of a high-performance, cloud-native simulation platform that powers reinforcement-learning training, controls validation, and robotics development at scale. You will shape distributed systems capable of running millions of parallel simulation experiences daily while mentoring senior engineers.

Build the ML infrastructure that turns multimodal robot and simulation data into qualified models deployed on Apollo humanoid robots. This hands-on senior role covers large-scale data pipelines, evaluation and simulation, model lifecycle services, and researcher developer tooling.

Robotics Test Engineer writing automated integration and regression tests that verify Apollo humanoid robot software. The role combines pytest-based test infrastructure, hardware-in-the-loop execution, defect investigation, and close collaboration with robotics and firmware engineers.

Lead the development of advanced humanoid robot controls for Apptronik’s Apollo platform, spanning whole-body control, mobile manipulation, hand control, and teleoperation. This staff-level role combines deep robotics expertise with hands-on software development and physical hardware deployment.

Senior Reinforcement Learning Engineer developing and deploying advanced learning algorithms for humanoid robot locomotion and manipulation. The role combines simulation, distributed training, sim-to-real transfer, and hands-on work with physical robotic hardware.

Staff MLOps Engineer owning the architecture and evolution of Apptronik’s platform for datasets, experiments, model artifacts, evaluation, and deployment to Apollo humanoid robots. This hands-on technical leadership role connects teleoperation data collection to reliable, auditable autonomy in the field.

Lead the development of learning-based dexterous manipulation and control software for Apptronik’s Apollo humanoid robot. This role bridges robotics research and reliable production deployment across simulation and physical hardware.