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.
As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and physical capabilities of our humanoid platforms. This role is dedicated to architecting sophisticated neural network topologies and implementing state-of-the-art RL algorithms to achieve world-class performance in whole-body locomanipulation. You will work alongside a multidisciplinary team to develop high-performance policies and optimized training pipelines that allow our robots to move and interact with the world with unprecedented fluidity. Beyond your technical contributions, you will play a key role in maintaining a high-velocity, ego-free engineering culture — sharing insights, participating in rigorous code reviews, and collaborating closely with hardware and controls teams to ensure our collective success on physical hardware.
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.
- Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.
- 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.
- Hands-on expertise (3+ 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.
- A PhD degree in Computer Science, Robotics, or a related field, or an MS degree in a similar field with 2+ years industry experience.
- 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.
- Prolonged periods of sitting at a desk and working on a computer
- Vision to read printed materials and a computer screen
- Hearing and speech to communicate
*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.
Skills Required
- 3+ years hands-on experience with RL frameworks (e.g., PyTorch, JAX)
- Experience with high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
- Mastery of Python for prototyping and strong proficiency in C++ for performant deployable code
- Experience building or using large-scale, distributed training pipelines and optimizing them
- Strong theoretical understanding of modern reinforcement learning (imitation learning, model-based RL, sim-to-real)
- Strong intuition for robot dynamics and controls theory and ability to apply to learning-based approaches
- Proven track record deploying learning-based policies on physical robotic systems (legged robots or manipulators)
- PhD in Computer Science, Robotics, or related field OR MS plus 2+ years industry experience
- Demonstrated experience mentoring or providing technical guidance to other engineers
- Strong publication record in relevant conferences or journals (CoRL, RSS, ICRA)
Apptronik Compensation & Benefits Highlights
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Healthcare Strength — Medical coverage is described as broad, with UnitedHealthcare plan choices (HDHP, PPO, HMO) and an employer-paid portion of premiums, alongside FSA and mental health benefits. This scope points to solid core medical support for employees.
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Parental & Family Support — Parental leave, family medical leave, and an onsite Mother’s Room are highlighted, with additional family-oriented events noted. These offerings indicate meaningful support for caregivers and families.
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Leave & Time Off Breadth — Flexible or open PTO is paired with paid holidays and sick days, and a hybrid setup enables at least two remote days per week where roles allow. Together, these policies signal ample time-off mechanisms and schedule flexibility.
Apptronik Insights
What We Do
Apptronik is building robots for the real world to improve human quality of life and to help solve the ever-increasing labor shortage problem. Our team has been building some of the most advanced robots on the planet for years, dating back to the DARPA Robotics Challenge. We apply our expertise across the full robotics stack to some of the most important and impactful problems our society faces, and expect our products and technology to change the world for the better. We value passion, creativity, and collaboration to help us overcome existing technological barriers in the industry to create truly innovative products.
Why Work With Us
At Apptronik, we don't see a future where man competes against machine. Instead, we envision a harmonious world where man and machine coexist. Our mission statement, "It is not Man vs. Machine, but Man + Machine," encapsulates our belief that the synergy between humans and robots will pave the way for a brighter, more advanced future.
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Apptronik Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
