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Robotic Imitation Learning Jobs (NOW HIRING)

Research and develop techniques around RL, imitation learning, video-action and world models to ... M.S/Ph.D degree in robotics, vision, computer science, mechanical engineering, electrical ...

Develop learning systems for contact-rich robotic manipulation using real-world demonstrations. * Design and deploy imitation learning, reinforcement learning, and policy optimization approaches for ...

Explore imitation learning, behavior cloning, diffusion policies, transformers, and RL * Use teleoperation, demonstration, and robot interaction data for training and fine-tuning Vision-Based ...

Explore imitation learning, behavior cloning, diffusion policies, transformers, and RL * Use teleoperation, demonstration, and robot interaction data for training and fine-tuning Vision-Based ...

Develop learning systems for contact-rich robotic manipulation using real-world demonstrations. * Design and deploy imitation learning, reinforcement learning, and policy optimization approaches for ...

Determine the appropriate combination of imitation learning, reinforcement learning, perception, control, and other learning approaches needed to advance robotic capabilities. * Establish the ...

Determine the appropriate combination of imitation learning, reinforcement learning, perception, control, and other learning approaches needed to advance robotic capabilities. * Establish the ...

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Robotic Imitation Learning information

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How much do robotic imitation learning jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for robotic imitation learning in the United States is $28.24, according to ZipRecruiter salary data. Most workers in this role earn between $22.12 and $32.93 per hour, depending on experience, location, and employer.

What is robotic imitation learning?

Robotic imitation learning is a technique in robotics where robots learn to perform tasks by observing and mimicking human actions or demonstrations. Instead of being explicitly programmed, the robot uses data from demonstrations to infer the necessary actions or behaviors. This approach can help robots learn complex tasks more efficiently and adapt to new situations by leveraging human expertise. It is widely used in areas such as manufacturing, service robotics, and autonomous vehicles.

What are some typical challenges faced when working on robotic imitation learning projects, and how can they be addressed?

Professionals in robotic imitation learning often encounter challenges such as collecting high-quality demonstration data, ensuring generalization across varied tasks, and managing the sim-to-real gap when transferring learned behaviors from simulation to physical robots. Addressing these challenges typically involves developing robust data collection pipelines, leveraging domain adaptation techniques, and collaborating closely with hardware engineers to fine-tune models in real-world environments. Teamwork with researchers specializing in computer vision, reinforcement learning, and robotics hardware is crucial for overcoming these hurdles and achieving reliable robot performance.

What are the key skills and qualifications needed to thrive in robotic imitation learning, and why are they important?

To thrive in Robotic Imitation Learning, you need a strong background in robotics, machine learning, and computer science, often supported by an advanced degree in a related field. Familiarity with programming languages like Python or C++, machine learning frameworks (e.g., TensorFlow, PyTorch), and robotics simulation tools such as ROS or Gazebo is essential. Strong problem-solving skills, creativity, and effective communication are crucial soft skills for developing innovative solutions and collaborating with multidisciplinary teams. These skills and qualities are important to advance the field, ensure robust algorithm development, and enable efficient deployment of robotic systems in real-world environments.

What is the difference between Robotic Imitation Learning vs Robotic Software Engineer?

AspectRobotic Imitation LearningRobotic Software Engineer
Required CredentialsAdvanced degrees in AI, Robotics, or Computer ScienceBachelor's or Master's in Software Engineering, Computer Science, or related fields
Work EnvironmentResearch labs, AI development teams, robotics startupsIndustrial, research, or manufacturing settings involving robotics software development
Industry UsageDeveloping algorithms for robots to learn behaviors through imitationDesigning, coding, and maintaining robotic control software

Robotic Imitation Learning focuses on creating algorithms that enable robots to learn behaviors by mimicking demonstrations, often requiring expertise in AI and machine learning. In contrast, a Robotic Software Engineer develops and maintains the software systems that control robots, emphasizing programming skills and software development. Both roles are essential in robotics but serve different functions within the industry.

What other helpful pages are available for Robotic Imitation Learning?

Other pages related to Robotic Imitation Learning:

Infographic showing various Robotic Imitation Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $58,744 per year, or $28.2 per hour.

Robotics Engineer/Researcher - Robot Learning - Imitation Learning, Foundation Models, RL

Palo Alto, CA • On-site

Other

Medical, Dental, Vision

Posted 25 days ago


Key responsibilities

  • Design and implement scalable training pipelines for general-purpose manipulation policies

  • Train large multimodal policies and push them to state-of-the-art on real tasks

  • Build the data engine behind the models, including demonstration collection, curation, filtering, and dataset design


Job description

Robot Learning Researcher/Engineer (Imitation Learning, Foundation Models, RL)

Join our team to push the frontier of robot learning. You'll train general-purpose control policies at scale - spanning imitation learning and large multimodal models - build the data and evaluation pipelines that make them work, and take policies from training runs to real robots. We care more about your ability to train models that work than about any particular robot, task, or sensor you've used before.

Requirements
  • 01 MS or PhD in Robotics, Computer Science, Machine Learning, or related field-or equivalent experience
  • 02 Strong track record training neural networks end-to-end: you can take a model from idea to a working, debugged, reproducible result
  • 03 Experience developing robot learning policies like diffusion policies, 3D policies, vision-language-action models, or video action models
  • 04 Experience training models at scale: multi-GPU/multi-node training, large datasets, long runs, and debugging throughput, stability, and scaling behavior
  • 05 Strong software engineering skills in Python and PyTorch or JAX in Linux environments (C++ a plus)
  • 06 Experience building large-scale data pipelines for robot learning - demonstration collection, curation, filtering, and dataset design
  • 07 Comfortable training policies with high-dimensional action spaces and multimodal observations (vision, proprioception, language)
  • 08 Rigorous about evaluation: designing benchmarks, running ablations, and drawing correct conclusions from noisy real-world results
  • 09 (+) Hands-on robotics experience - hardware bring-up, teleoperation and real-world data collection, on-robot deployment
  • 10 (+) Experience with high-performance simulation (MuJoCo, Isaac Gym/Lab) and sim2real techniques (domain randomization, dynamics adaptation, residual policy learning)
  • 11 (+) Familiarity with contact-rich or dexterous manipulation, tactile sensing, or differentiable simulation
Details & responsibilities
  • 01 Design and implement scalable training pipelines for general-purpose manipulation policies
  • 02 Train large multimodal policies - diffusion, 3D, vision-language-action, and video action models - and push them to state-of-the-the advantage on real tasks
  • 03 Build the data engine behind the models: demonstration collection, curation, filtering, and dataset design at scale
  • 04 Integrate visual, proprioceptive, and tactile feedback into policy architectures
  • 05 Take policies from training runs to real hardware, closing the loop with on-robot deployment and iteration
  • 06 Collaborate across AI, hardware, and perception teams to build closed-loop manipulation systems
  • 07 Publish or contribute to cutting-edge research while delivering production-quality control stacks
  • 01 Competitive salary and meaningful equity
  • 02 Full health, dental, and vision insurance
  • 03 Access to custom-built dexterous robots
  • 04 Collaboration with leading researchers in robotics and AI
  • 05 Backed by YC and top-tier investors
  • 06 High-ownership role with the opportunity to lead core initiatives in real-world robot learning
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