Senior Reinforcement Learning Engineer

The Robot Age; Sunnyvale (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Apptronik in Sunnyvale, CA is seeking a senior reinforcement learning engineer to advance learned locomotion and manipulation on the Apollo humanoid platform.

You will build and deploy RL policies, run sim-to-real loops, and mentor teammates while improving training pipelines and high-throughput simulation.

This role emphasizes hands-on implementation and collaboration across autonomy, controls, and hardware teams.

Qualifications

• Senior-level experience with reinforcement learning for robotics.
• Strong sense of sim-to-real transfer and hardware testing constraints.
• Ability to build reliable model-training and evaluation pipelines.
• Collaborative mindset across autonomy, controls, and hardware teams.

Responsibilities

• Build and deploy reinforcement learning policies for humanoid robot behavior.
• Run the loop from simulation experiments to physical hardware results.
• Improve distributed training and high-throughput simulation workflows.
• Mentor teammates working on embodied learning problems.

Skills

Reinforcement learning

Robotics

Collaboration

Mentoring

Job description

What the role actually is

Apptronik is hiring a senior reinforcement learning engineer in Sunnyvale. Like the Austin version, this role targets learned locomotion and manipulation on the Apollo humanoid platform.

The listing points to hands-on implementation, simulation-to-hardware transfer, training pipeline work, and technical mentoring.

What you would work on

• Build and deploy reinforcement learning policies for humanoid robot behavior
• Run the loop from simulation experiments to physical hardware results
• Improve distributed training and high-throughput simulation workflows
• Mentor teammates working on embodied learning problems

What they are asking for

• Senior-level experience with reinforcement learning for robotics
• Practical judgment about sim-to-real gaps and hardware test constraints
• Ability to build reliable model-training and evaluation workflows
• Collaborative habits across autonomy, controls, and hardware teams

Why this one is worth a look

The Sunnyvale opening suggests Apptronik is building robot-learning capacity beyond its Austin base. For candidates in the Bay Area, it is a direct route into humanoid RL without relocating to Texas.