Research Scientist Intern

Clone; Mountain View (CA); On-site; USD 35,000 - 50,000; Full time

Benefits offered by this job

Paid holidays

Direct access to hardware

Job summary

Clone in Mountain View is seeking an intern focused on policy training and robotics. As part of the team, you will work on enhancing simulation fidelity and deploying RL policies on real hardware, contributing to advancements in human-like robotic systems.

The role requires strong foundations in reinforcement learning and hands-on experience with technologies like PyTorch or JAX. You will have direct access to muscle-driven robotic systems and considerable project guidance.

Qualifications

• Currently pursuing or recently completed a BS, MS, or PhD in relevant fields.
• Strong fundamentals in reinforcement learning, ideally with hands-on experience.
• Experience with physics simulators and a willingness to work with real hardware.

Responsibilities

• Train RL policies in simulation for dexterous, in-hand manipulation.
• Build and harden sim-to-real pipelines for robotics.
• Deploy policies on real hardware and debug issues.

Skills

Reinforcement learning

Deep learning

PyTorch

JAX

Sim-to-real transfer

Dexterous manipulation

Education

BS, MS, or PhD in CS, Robotics, EE, ML, or related field

Tools

MuJoCo

Isaac

Physical simulators

Job description

You'll work end-to-end: from training policies in simulation all the way to debugging what breaks when the policy meets real hardware. The work spans simulation fidelity, policy learning, and empirical bring-up on muscle-driven robots.

Policy training & simulation

• Train RL policies in simulation — MuJoCo, Isaac, or similar — fordexterous, in-hand manipulationon musculotendon-driven robots.
• Improve the fidelity of our simulation models ofcompliant, high-DOF musculoskeletal systems: the closer the sim, the smaller the reality gap.
• Designmetrics and benchmarksto evaluate policies in open loop, in simulation, and on the real robot — so progress is measurable at every stage.
• Build and hardensim-to-real pipelines: domain randomization, system identification, and actuator modeling for MTUs and tendon routing.
• Deploy policies on real hardware, thendebug what breaks: latency, friction, hysteresis, sensing, and everything the simulator didn't warn you about.

Requirements

We care about fundamentals and hands-on experience with real systems. Strong theoretical grounding matters, but so does comfort operating outside the simulator.

Required

• Currently pursuing or recently completed aBS, MS, or PhDin CS, Robotics, EE, ML, or a related field.
• Strong fundamentals inreinforcement learning— e.g. PPO, SAC — and deep learning, with hands-onPyTorch or JAX.
• Experiencetraining RL policies, ideally for robotics or continuous control.
• Comfort with aphysics simulator: MuJoCo, Isaac Sim / Lab, Brax, Genesis, or similar.
• Willingness to work withreal hardware— robotics is an empirical science, and debugging a policy on real robot is part of the job.
• Sim-to-real transfer, domain randomization, or system identification.
• Dexterous manipulation, contact-rich control, or tendon-driven systems.
• Published work at ICRA, IROS, CoRL, RSS, ICML, NeurIPS, CVPR, ECCV, ICCV, or similar.

What We Offer

You'll be contributing to an open research problem with direct impact on the robot we're building at Clone, on a path to realize the most human-like and human-level android in the world.

• End-to-end ownership.From simulation to real hardware deployment — you'll own the full stack for your track, not just the training loop.
• Hardware access.Direct access to muscle-driven robotic hands and the full sensor stack — not a simulation-only role.
• Mountain View lab.On-site in the Bay Area, alongside the core Intelligence & Behavior and Demos teams.
• Compute.The GPU resources to train the policies the work actually requires.
• Project guidance.Close collaboration with researchers who care about sim-to-real, musculoskeletal systems, and getting things to work on real hardware.
• Scaling. Our company is constantly growing. You will become part of an international team with wide development opportunities.
• 3-month internship (including 8 days of paid holidays) with the possibility to extend to a full-time job.