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Helper Reinforcement Learning Jobs in California

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Helper Reinforcement Learning information

What is the difference between Helper Reinforcement Learning vs Data Scientist?

AspectHelper Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of reinforcement learningDegree in Data Science, Statistics, Computer Science; proficiency in programming and analytics
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, consulting firms, tech companies
Industry UsageAI development, machine learning projectsData analysis, predictive modeling, business insights
Common Search/ComparisonHelper Reinforcement Learning vs Data Scientist

Helper Reinforcement Learning focuses on developing algorithms that enable machines to learn through interactions, often requiring knowledge of reinforcement learning techniques. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve programming and data handling, Helper Reinforcement Learning is more specialized in AI algorithm development, whereas Data Scientists work broadly across data analysis and modeling in various industries.

What are the most commonly searched types of Reinforcement Learning jobs in California? The most popular types of Reinforcement Learning jobs in California are:
What cities in California are hiring for Helper Reinforcement Learning jobs? Cities in California with the most Helper Reinforcement Learning job openings:

Reinforcement learning engineer

Dexmate

Santa Clara, CA • On-site

Other

This job post has expired today. Applications are no longer accepted.


Job description

Reinforcement Learning Expert

Dexmate is building the foundation for physical AI — a unified platform that combines high-quality robotic hardware with a universal Physical AI OS, making robots as easy to build and deploy as software. Today, robotics is fragmented, slow, and closed: most builders are forced to reinvent the same stack again and again, and most ideas never make it past the prototype stage. We exist to change that. Our mission is to democratize robotics by lowering the barrier to entry, delivering a plug-and-play platform for developers, researchers, and enterprises, and cultivating an open ecosystem that accelerates the evolution of physical AI. If you want to help shape the next layer of human capability — and believe the future of robotics should be built together, not in isolation — we'd love to build it with you.

Role Overview

We're seeking Reinforcement Learning experts to develop and deploy cutting-edge RL algorithms that enhance our robots' capabilities.

Responsibilities

  • Design and implement reinforcement learning algorithms for various robotics tasks
  • Develop and optimize RL training pipelines in both simulation and real-world environments
  • Collaborate with robotics engineers to integrate RL models into production systems
  • Conduct experiments to evaluate and improve algorithm performance
  • Scale training infrastructure for efficient learning across multiple robots

Required Qualifications

  • Strong experience with reinforcement learning (PPO, SAC, TD3, DDPG, etc.)
  • Hands-on experience with robotics systems (simulation or real robots)
  • Proven track record applying RL to manipulation, locomotion, or navigation tasks
  • Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX)
  • Strong understanding of robot kinematics, dynamics, and control
  • Experience with GPU-based simulation such as Isaac Gym, Isaac Lab, SAPIEN, etc.

Preferred Qualifications

  • Experience with distributed RL training systems
  • Experience with sim-to-real transfer techniques
  • Publications in robotics or RL conferences (CoRL, ICRA, RSS, NeurIPS, ICLR, ICML, etc.)