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Robotics Manipulation Reinforcement Learning Jobs

Figure

Helix AI Engineer, Robot Learning

San Jose, CA · On-site

$200K - $400K/yr

Experience with behavior cloning, reinforcement learning , or related learning-based manipulation methods * Proficiency in Python and/or C++ for robotics and ML systems * Experience with modern deep ...

Figure

Helix AI Engineer, Robot Learning

San Jose, CA · On-site

$200K - $400K/yr

Experience with behavior cloning, reinforcement learning , or related learning-based manipulation methods * Proficiency in Python and/or C++ for robotics and ML systems * Experience with modern deep ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will focus on achieving state-of-the-art performance on humanoid robots, leveraging expertise in reinforcement learning to solve locomotion and manipulation ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... and manipulation challenges and deliver breakthrough results on physical hardware. The primary ... Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co ...

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Robotics Manipulation Reinforcement Learning information

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How much do robotics manipulation reinforcement learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for robotics manipulation reinforcement learning in the United States is $96,000.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,000.00 and $102,000.00 per year, depending on experience, location, and employer.

What is robotics manipulation reinforcement learning?

Robotics Manipulation Reinforcement Learning is a field of artificial intelligence where robots learn to interact with and manipulate objects in their environment through trial and error, using feedback to improve their performance. This involves developing algorithms that enable robots to autonomously acquire complex motor skills, such as grasping, pushing, or assembling objects, without explicit human programming for every task. Reinforcement learning provides the framework for robots to optimize their actions by receiving rewards or penalties based on their success in manipulating objects, making them more adaptable to new and unstructured environments.

What are some common challenges faced when applying reinforcement learning in robotics manipulation tasks?

A common challenge in robotics manipulation with reinforcement learning (RL) is dealing with the complexity and unpredictability of real-world environments. Unlike simulations, physical robots must handle noisy sensors, actuator delays, and unexpected interactions with objects. Training RL models can also be time-consuming and data-intensive, requiring robust simulation environments or safe real-world data collection strategies. Collaboration with hardware engineers and domain experts is often essential to troubleshoot issues and optimize learning efficiency. Successful practitioners are adaptable and proactive in bridging the gap between theory and real-world robotic performance.

What are the key skills and qualifications needed to thrive as a robotics manipulation reinforcement learning engineer, and why are they important?

To thrive as a Robotics Manipulation Reinforcement Learning Engineer, you need a solid background in robotics, machine learning (especially reinforcement learning), computer science, and typically an advanced degree such as a Master's or PhD. Experience with programming languages like Python or C++, frameworks such as TensorFlow or PyTorch, and robotics middleware like ROS are commonly required, along with familiarity with simulation environments like Gazebo or Mujoco. Strong problem-solving, collaboration, and communication skills set top performers apart as they integrate complex algorithms into real-world robotic systems. These skills and qualities are crucial for developing effective, adaptive robotic solutions that perform sophisticated manipulation tasks in dynamic environments.

What is the difference between Robotics Manipulation Reinforcement Learning vs Robotics Software Engineer?

AspectRobotics Manipulation Reinforcement LearningRobotics Software Engineer
Required CredentialsAdvanced degrees in AI, Robotics, or related fields; knowledge of reinforcement learningBachelor's or master's in Computer Science, Robotics, or Software Engineering
Work EnvironmentResearch labs, AI startups, academia focusing on machine learning applications in roboticsIndustrial settings, manufacturing, or tech companies developing robotic systems
Industry UsageDeveloping algorithms for robotic manipulation tasks using reinforcement learningBuilding, testing, and deploying robotic software systems

Robotics Manipulation Reinforcement Learning specialists focus on creating algorithms that enable robots to learn manipulation tasks through reinforcement learning techniques. In contrast, Robotics Software Engineers develop and maintain the software systems that control robotic hardware. While both roles require programming skills, the former emphasizes machine learning and AI research, whereas the latter concentrates on software development and integration in robotic applications.

What other helpful pages are available for Robotics Manipulation Reinforcement Learning?

Other pages related to Robotics Manipulation Reinforcement Learning:

Infographic showing various Robotics Manipulation Reinforcement Learning job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Hybrid job distribution, with an average salary of $96,000 per year, or $46.2 per hour.

Helix AI Engineer, Robot Learning

San Jose, CA • On-site

Figure
See more jobs at Figureexternal

$200K - $400K/yr

Full-time

Re-posted 12 days ago

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA.

We are looking for a Helix AI Engineer, Robot Learning with a strong robotics learning background to help develop and improve our visuomotor manipulation policies, with a heavy emphasis on real-robot deployment.

Responsibilities
  • Design, train, evaluate, and deploy learning-based visuomotor policies for humanoid robot manipulation
  • Develop manipulation behaviors such as grasping, pick-and-place, object reorientation, door opening, bimanual manipulation, and basic assembly
  • Apply and extend techniques including behavior cloning, reinforcement learning, and VLA reasoning
  • Train models that are robust to real-world challenges such as sensor noise, partial observability, contact dynamics, and environment variability
  • Own the full pipeline from data collection on real robots to model training, evaluation, and deployment
  • Work closely with simulation and digital twin tooling where useful, while prioritizing real-world performance and transfer
  • Collaborate with perception, controls, systems, and hardware teams to integrate policies into a full autonomy stack
  • Evaluate tradeoffs between learning-based and classical approaches and make principled design decisions
  • Write high-quality, well-tested software that ships to and runs reliably on physical humanoid robots
  • Partner with integration and testing teams to continuously improve robustness, performance, and deployment velocity
Requirements
  • Hands-on experience developing and deploying robot learning systems on real robots
  • Strong background in robot manipulation and visuomotor control
  • Experience with behavior cloning, reinforcement learning, or related learning-based manipulation methods
  • Proficiency in Python and/or C++ for robotics and ML systems
  • Experience with modern deep learning frameworks (e.g., PyTorch)
  • Ability to design experiments, analyze failures, and iterate quickly in real-world robotic systems
  • Solid understanding of the tradeoffs between classical robotics approaches and learning-based methods
  • Thrive in fast-paced, ambiguous environments where solutions require exploration and ownership
Bonus Qualifications
  • Experience deploying learning-based manipulation systems in commercial or production robotic systems
  • Prior work on humanoids or highly dexterous robotic platforms
  • Publication record in robot learning, manipulation, or embodied AI
  • Experience leading projects or mentoring other engineers
  • Passion for building autonomous humanoid robots that operate in the real world

The US base salary range for this full-time position is between $200,000 - $400,000.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended. 

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