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Reinforcement Learning Robotics Jobs in California

Skild AI

Research Scientist

San Mateo, CA · On-site

Deep technical knowledge, and research experience in deep learning, reinforcement learning, robotics, or computer vision. * Deep understanding of state-of-the-art machine learning techniques and ...

Agility Robotics

Staff AI Research Engineer

Fremont, CA

$216K - $338K/yr

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

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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 California as of June 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Hybrid job distribution.
Machine Learning Engineer - Reinforcement Learning

Machine Learning Engineer - Reinforcement Learning

Pony.ai

Fremont, CA • On-site

Full-time

Posted 16 days ago

Job description

Job Summary:
Pony.ai is a global leader in autonomous mobility, recognized for its innovative technologies and services in the field. The role involves building scalable systems for training large generative models, implementing reinforcement learning methods, and shipping deep learning solutions to enhance self-driving behaviors.
Responsibilities:
• Build scalable systems for training and fine-tuning large generative models that produce realistic, informative driving behaviors for evaluation and scenario coverage.
• Implement and iterate on RL-style methods: algorithms, reward / preference objectives, and training setups suited to high-fidelity, insightful behaviors in simulation-aligned workflows (closed-loop evaluation mindset).
• Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led triaging, automate high-volume workflows, and support nuanced analysis of self-driving behavior to surface critical anomalies.
• Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and iteration of models used to judge performance across large real-world exposure.
• Design and evolve data + evaluation systems inspired by RL from human preferences (RLHF) and related paradigms—turning preference/judgment signals into repeatable, scalable training and evaluation loops.
• Partner broadly with teams such as Prediction, Planning, Research, and platform/engineering leads to land cross-cutting improvements with clear metrics.
Qualifications:
Required:
• M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field—or equivalent practical experience.
• Hands-on experience building and applying ML in production-grade settings, with a strong RL component (policy learning, preference/feedback optimization, or offline/online RL pipelines).
• Depth in deep learning, sequence modeling, and generative models.
• Demonstrated impact via strong publications or a clear history of shipping impactful ML systems end-to-end.
• Experience with large-scale distributed training and large-scale data processing.
• Ability to lead ambiguous technical work from problem framing through reliable delivery.
Preferred:
• Background in autonomous vehicles, robotics, or complex simulation environments.
• Strong grasp of modern RL and post-training techniques in LLM, dLLM, VLA and video generations.
• Hands-on integration of simulation platforms with ML training and evaluation workflows.
• Python fluency and frameworks such as PyTorch.
• Experience defining and operating metrics for complex, safety-critical AI systems.
• Technical leadership: influencing stakeholders, aligning teams, and raising the bar for evaluation rigor.
• Excellent communication—simple explanations of complex trade-offs.
Company:
Pony.ai develops autonomous driving technology for vehicles that operates using artificial intelligence and machine learning. Founded in 2016, the company is headquartered in Fremont, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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