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Reinforcement Learning Robotics Jobs in Pasadena, TX

Bot Auto

Algorithm Engineer, Reinforcement Learning

Houston, TX · On-site

$56 - $77/hr

  • Medical

  • PTO

You will work at the intersection of Multi-Agent Reinforcement Learning (MARL) and safety-critical ... MS or PhD in Computer Science, Robotics, or a related quantitative field. * Scientific Intuition:

Bot Auto

Algorithm Engineer, Reinforcement Learning

Houston, TX · On-site

$56 - $77/hr

  • Medical

  • PTO

You will work at the intersection of Multi-Agent Reinforcement Learning (MARL) and safety-critical ... MS or PhD in Computer Science, Robotics, or a related quantitative field. * Scientific Intuition:

Persona AI

Autonomy Engineer

Houston, TX · On-site

$97K - $128K/yr

  • Medical

  • PTO

Experience with semantic mapping and scene understanding for autonomous mobile robots * Experience with NVIDIA Isaac Lab for reinforcement learning environment setup and training * Experience with ...

Persona AI

Autonomy Engineer

Houston, TX · On-site

$97K - $128K/yr

  • Medical

  • PTO

Experience with semantic mapping and scene understanding for autonomous mobile robots * Experience with NVIDIA Isaac Lab for reinforcement learning environment setup and training * Experience with ...

NAVA Software Solutions

Sr AI Agentic Engineer

Spring, TX · On-site

$93K - $127K/yr

This role works at the intersection of LLMs, systems engineering, and applied machine learning ... Apply reinforcement or feedback-driven optimization where applicable, including human-in-the-loop ...

All JobsReinforcement Learning Robotics JobsReinforcement Learning Robotics Jobs in Pasadena, TX

Reinforcement Learning Robotics information

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 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 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.

What job categories do people searching Reinforcement Learning Robotics jobs in Pasadena, TX look for?

The top searched job categories for Reinforcement Learning Robotics jobs in Pasadena, TX are:

What cities near Pasadena, TX are hiring for Reinforcement Learning Robotics jobs?

Cities near Pasadena, TX with the most Reinforcement Learning Robotics job openings:

Reinforcement Learning Robotics jobs near you

Reinforcement Learning Engineer, Grasping

Persona AI

Houston, TX • On-site

Full-time

Re-posted 8 hours ago

Job description

Job Summary:
Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. They are seeking a Reinforcement Learning Engineer to join their Manipulation team, focusing on developing reliable grasping policies for high-DOF robotic hands.
Responsibilities:
• Train and iterate on reinforcement learning policies for complex grasping tasks including functional grasping, tool use, in-hand manipulation, and environment interaction.
• Implement and refine sim-to-real transfer pipelines to bridge the gap between simulation and physical robotic hand performance.
• Develop reward functions, curriculum strategies, and training environments in MuJoCo and Isaac Lab.
• Run experiments on real robots alongside simulation, evaluating and debugging policy behavior on hardware.
• Monitor, evaluate, and adapt state-of-the-art research in learning-based grasping to deploy on our humanoid platform.
• Collaborate with the rest of the software team to deploy end-to-end grasping systems.
• Benchmark and evaluate grasp policies across object diversity, clutter scenes, and real-world uncertainties.
• Integrate tactile sensing and feedback into grasp policies for robust, force-aware manipulation.
Qualifications:
Required:
• BS, MS, or PhD in Robotics, Computer Science, Machine Learning, or a related field.
• 2+ years of hands-on experience in reinforcement learning for robotic manipulation; exceptional recent graduates from relevant research labs will be considered.
• Demonstrated ability to read, understand, and implement ideas from recent robotics and machine learning research.
• Hands-on experience training RL agents for robotic manipulation tasks, including reward shaping and policy evaluation.
• Experience with sim-to-real transfer: domain randomization, physics tuning, or real-world policy validation on hardware.
• Proficiency in Python and deep learning frameworks (PyTorch, JAX), along with RL libraries such as rsl_rl or skrl.
• Experience preparing meshes and collision geometries for RL environments in simulators such as MuJoCo and/or Isaac Sim.
Preferred:
• Experience deploying RL-trained policies on physical robotic hands.
• Experience with tactile sensors and integrating tactile feedback into learned grasp policies.
• Experience with contact-rich manipulation and force/torque estimation.
• Familiarity with other learning-based approaches such as behavior cloning, imitation learning, or diffusion-based policy methods.
• Publications or project work at top-tier venues (CoRL, RSS, ICRA) on grasping or dexterous manipulation.
• Experience in a humanoid robot startup environment.
Company:
Persona AI is a robotics company that provides robotic solutions. Founded in 2024, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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