Senior Reinforcement Learning Engineer

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

Job summary

Apptronik, a human-centered robotics company, is seeking a Senior Reinforcement Learning Engineer in Sunnyvale, CA to push state-of-the-art RL for locomotion and manipulation on our humanoid robots. You will prototype in simulation, transfer policies to real hardware, and scale training pipelines while mentoring junior engineers.

You'll apply deep RL, model-based techniques, and sim-to-real transfer to enable robust robot behavior, collaborating with robotics and hardware teams to advance

Qualifications

• Deep, hands-on expertise with RL frameworks and high-fidelity simulators.
• Proficiency in Python and C++ for rapid prototyping and deployment.
• Experience with large-scale distributed training pipelines.
• Strong understanding of modern RL, imitation learning, and sim-to-real.

Responsibilities

• Implement and deploy state-of-the-art RL for locomotion and manipulation on real robots.
• Prototype in simulation and transfer policies to physical hardware.
• Scale RL training pipelines for faster iteration and robustness.
• Mentor junior engineers and share RL best practices.
• Collaborate with robotics and hardware teams on system-level issues.
• Analyze hardware results to guide future directions.
• Develop motion retargeting pipelines from human data to robot trajectories.

Skills

PyTorch

JAX

MuJoCo

IsaacGym

Python

C++

Distributed training

Imitation learning

Model-based RL

Sim-to-real

Education

MS in CS/Robotics

PhD preferred

Job description

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.

We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.

JOB SUMMARY

The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
• Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
• Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
• Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
• Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
• Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
• Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.

SKILLS AND REQUIREMENTS

• Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
• Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
• Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
• A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
• A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
• A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.

EDUCATION and/or EXPERIENCE

• A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
• A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
• Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
• A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

PHYSICAL REQUIREMENTS

• Prolonged periods of sitting at a desk and working on a computer
• Must be able to lift 15 pounds at times
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate

Compensation: The annual compensation for this position is $230,000 - $260,000 (USD)

*This is a direct hire. Please, no outside Agency solicitations.

Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Technology Control Restrictions: As a person in this position will have access to technical data and/or computer software maintained by the Company, which includes export-controlled technical data and/or computer software, successful applicants must be eligible under U.S. export control regulations to access such information.

Voluntary Self-Identification of Disability

• Alcohol or other substance use disorder (not currently using drugs illegally)
• Autoimmune disorder, for example, lupus, fibromyalgia, rheumatoid arthritis, HIV/AIDS
• Blind or low vision
• Cancer (past or present)
• Cardiovascular or heart disease
• Celiac disease
• Cerebral palsy
• Deaf or serious difficulty hearing
• Diabetes
• Disfigurement, for example, disfigurement caused by burns, wounds, accidents, or congenital disorders
• Epilepsy or other seizure disorder
• Gastrointestinal disorders, for example, Crohn's Disease, irritable bowel syndrome
• Intellectual or developmental disability
• Mental health conditions, for example, depression, bipolar disorder, anxiety disorder, schizophrenia, PTSD
• Missing limbs or partially missing limbs
• Mobility impairment, benefiting from the use of a wheelchair, scooter, walker, leg brace(s) and/or other supports
• Nervous system condition, for example, migraine headaches, Parkinson’s disease, multiple sclerosis (MS)
• Neurodivergence, for example, attention‑deficit/hyperactivity disorder (ADHD), autism spectrum disorder, dyslexia, dyspraxia, other learning disabilities
• Partial or complete paralysis (any cause)
• Pulmonary or respiratory conditions, for example, tuberculosis, asthma, emphysema
• Short stature (dwarfism)
• Traumatic brain injury