Senior RL Engineer, Robotics & Real-World Deployment

Apptronik; Sunnyvale (CA); On-site; USD 230,000 - 260,000; Full time

Job summary

Apptronik is a human-centered robotics company building AI-powered robots to support humanity, starting with manufacturing and logistics. The Senior Reinforcement Learning Engineer will push state‑of‑the‑art learning for locomotion and manipulation on our humanoid platform, Apollo.

You will prototype in simulation, iterate rapidly, and deploy policies on physical hardware while mentoring junior engineers and shaping core RL infrastructure for scalable training.

Qualifications

• PhD or MS in Computer Science, Robotics, or related field; 2+ years industry experience preferred.
• Proven track record deploying learning-based policies on physical robots, especially legged robots or manipulators.
• Mentoring or guiding engineers in a team environment.
• Strong publication record in CoRL, RSS, ICRA is a plus.

Responsibilities

• Implement and deploy state-of-the-art RL algorithms on dynamic locomotion and manipulation tasks with physical hardware.
• Prototype in simulation and transfer/fine-tune policies on the robot.
• Scale RL training pipelines for faster iteration and hardware-in-loop results.
• Mentor junior engineers and share RL and software development best practices.
• Collaborate with robotics and hardware teams to diagnose issues and co-develop solutions.
• Analyze hardware results and guide future technical directions.

Skills

Python

C++

PyTorch

JAX

MuJoCo

IsaacGym

Reinforcement learning

Sim-to-real

Distributed training

Robot dynamics

Education

PhD or MS in Computer Science, Robotics or related field

2+ years industry experience

Tools

MuJoCo

IsaacGym

PyTorch

JAX

Job description

Apptronik is a human-centered robotics company building AI-powered robots to support humanity, starting with manufacturing and logistics. The Senior Reinforcement Learning Engineer will push state‑of‑the‑art learning for locomotion and manipulation on our humanoid platform, Apollo.

You will prototype in simulation, iterate rapidly, and deploy policies on physical hardware while mentoring junior engineers and shaping core RL infrastructure for scalable training.