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Reinforcement Learning Robotics Jobs (NOW HIRING)

Dexmate

Reinforcement learning engineer

Santa Clara, CA · On-site

Dexmate is building the foundation for physical AI -- a unified platform that combines high-quality robotic hardware with a universal Physical AI OS. They are seeking Reinforcement Learning experts ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. The Senior Reinforcement Learning Engineer will leverage their expertise in ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. The Senior Reinforcement Learning Engineer will focus on achieving ...

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Reinforcement Learning Robotics information

What are some common challenges faced when implementing reinforcement learning algorithms in robotics projects?

One common challenge in this role is bridging the gap between simulation and real-world environments, as algorithms that perform well in simulation may not translate directly to physical robots due to unpredictable variables and hardware limitations. Additionally, ensuring the safety and stability of the robot during training is crucial, since trial-and-error learning can sometimes result in unintended behaviors or hardware damage. Collaboration with hardware engineers and domain experts is often necessary to fine-tune models, interpret results, and iterate on solutions. Overcoming these challenges requires patience, adaptability, and strong communication skills within a multidisciplinary team.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Robotics Engineer, and why are they important?

To thrive as a Reinforcement Learning Robotics Engineer, you need a strong background in robotics, machine learning, and programming, typically supported by a degree in computer science, engineering, or a related field. Expertise with frameworks like TensorFlow or PyTorch, experience with simulation environments (such as Gazebo or ROS), and familiarity with reinforcement learning algorithms are essential. Strong problem-solving skills, creativity, and effective communication set standout professionals apart in this rapidly evolving field. These skills enable engineers to develop intelligent robotic systems that adapt and learn efficiently, driving innovation and practical deployment in real-world environments.

What is reinforcement learning in robotics?

Reinforcement learning in robotics refers to a type of machine learning where robots learn to perform tasks through trial and error, receiving feedback from their actions in the form of rewards or penalties. This approach allows robots to autonomously develop complex behaviors by interacting with their environment, rather than relying solely on pre-programmed instructions. Reinforcement learning is especially useful for tasks that are difficult to model explicitly, such as walking, grasping, or navigation. Over time, the robot improves its performance by maximizing the cumulative reward, leading to more efficient and adaptive behaviors.

What is the difference between Reinforcement Learning Robotics vs Machine Learning Engineer?

AspectReinforcement Learning RoboticsMachine Learning Engineer
Required CredentialsDegree in Robotics, Computer Science, or related fields; knowledge of reinforcement learningDegree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms
Work EnvironmentRobotics labs, manufacturing, autonomous systemsTech companies, data-driven projects, software development
Industry UsageAutonomous robots, industrial automation, researchData analysis, predictive modeling, AI applications

Reinforcement Learning Robotics focuses on applying reinforcement learning techniques to control and optimize robotic systems, often in physical environments. Machine Learning Engineers develop algorithms for a broad range of applications, including data analysis and predictive modeling. While both roles require knowledge of machine learning, Reinforcement Learning Robotics emphasizes robotics and real-world interaction, whereas Machine Learning Engineers work across various industries with software-based solutions.

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Reinforcement Learning Robotics jobs near you
Infographic showing various Reinforcement Learning Robotics job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 98% Full Time, and 1% Temporary. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.
Research Scientist - Reinforcement Learning, Robotics

Research Scientist - Reinforcement Learning, Robotics

Applied Intuition

Sunnyvale, CA • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 21 days ago

Job description

Research Scientist - Reinforcement Learning, Robotics

Sunnyvale, California, United States

About Applied Intuition

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company's solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo.

We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office five days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.

About the Role and Team

We are looking for multiple passionate Research Scientists to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA.

Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business.

We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity.

At Applied Intuition, You Will:

  • Conduct research on reinforcement learning (RL) related topics including large-scale closed-loop RL and VLA post-training with applications to robotics with emphasis on dexterous manipulation
  • Diving into fundamental and relevant topics on RL with broader applications
  • Work closely with other Research Scientists and interns on research publications for submission to top-tier conferences
  • Collaborate with Research Engineers and engineering teams to test and deploy algorithms to our autonomy and robotics products

We're Looking For Someone Who Has:

  • Strong research record in the fields of RL and VLA post-training for robotics and autonomous systems, with publications in top-tier conferences or journals in the fields of computer vision, machine learning, and robotics
  • MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely-related fields
  • Passion for next-generation, scalable autonomy and robotics for real-world systems
  • Strong research skills and the ability to work both independently and collaboratively on projects
  • Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training

Nice To Have:

Hands-on experience in at least one of the following fields:

  • VLA post-training for autonomy or robotics
  • Large-scale closed-loop RL in robotic simulation
  • Large-scale RL training infrastructure

Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.

Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the location listed is: $126,000 - $423,000 USD annually.

Don't meet every single requirement? If you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.

Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws.

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