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Vice President Deep Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Required : • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX ... reinforcement learning, including deep expertise in areas like imitation learning, model-based RL ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Required : • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX ... reinforcement learning, including deep expertise in areas like imitation learning, model-based RL ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Required : • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX ... reinforcement learning, including deep expertise in areas like imitation learning, model-based RL ...

Barr Foundation

Vice President, Programs

Boston, MA

In deep partnership with Ali Noorani, the new President, and passionate colleagues and trustees ... They lead, mentor, manage and inspire the program and learning and evaluation teams, modeling and ...

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As of Jul 14, 2026, the average yearly pay for vice president deep reinforcement learning in the United States is $157,532.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,000.00 and $190,000.00 per year, depending on experience, location, and employer.
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Reinforcement Learning Engineer, Grasping

Persona AI

Houston, TX • On-site

Full-time

Posted 25 days 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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