Founding Research Scientist (ML/Robotics)

Fractal Labs Inc.; United States; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation

Equity options

Access to large-scale compute resources

Job summary

A pioneering robotics company in the United States seeks a Founding Research Scientist specializing in ML and robotics. In this role, you will lead groundbreaking research to develop sophisticated algorithms and control systems for humanoid robots in dynamic manufacturing environments. Candidates should hold a PhD or Master's and possess hands-on experience in deploying robotic policies. Join us to collaborate with leading experts and enjoy competitive compensation and equity opportunities.

Qualifications

• PhD or Master's with equivalent experience in ML, Robotics, CS, Physics, or related fields.
• Hands-on deployment of policies on physical robots.
• Strong engineering skills in Python + PyTorch/JAX.

Responsibilities

• Lead research on world models, VLAs, and Deep RL algorithms.
• Design physics-consistent generative models for control.
• Deploy policies on real humanoids and enhance reliability.

Skills

Deep RL

Python

Scaled training systems

Humanoid robots

Multi-contact control

Education

PhD or Master's in ML, Robotics, CS, Physics or related fields

Tools

PyTorch

JAX

Isaac Sim

MuJoCo

Job description

Founding Research Scientist (ML/Robotics)

We are building the next generation of physically reliable, safety-critical humanoid robots for real manufacturing environments. You will lead research at the intersection of world models, VLAs, and Deep RL, developing algorithms and control systems that enable humanoids to perform robust manipulation, locomotion, and long-horizon task execution in dynamic, unstructured settings. Your work will define our autonomy roadmap and directly impact real-world deployment.

What You Will Do

Core Research

• Develop next-gen world models, VLA policies, and Deep RL algorithms for dynamics understanding, uncertainty reasoning, and long-horizon planning.
• Design physics-consistent generative models (e.g., Genie, V-JEPA, latent dynamics) for predictive control and sim-to-real transfer.
• Build visuomotor policies for industrial manipulation and locomotion.
• Improve robustness, fault tolerance, distribution-shift resilience, and safety across behaviors.
• Apply hybrid approaches (model-based RL, diffusion policies, moment matching, MPC hybrids) to stabilize control.

Simulation & Deployment

• Use Isaac Sim, MuJoCo, and custom simulators to generate training data and evaluate policies.
• Build physics-aware pipelines integrating contacts, proprioception, force/torque feedback, and language-conditioned planning.
• Deploy policies on real humanoids (Unitree-class or custom), debugging failure modes and enhancing reliability and recovery behaviors.

What We’re Looking For

• PhD or Master's with equivalent experience in ML, Robotics, CS, Physics, or related fields.
• Deep RL (actor-critic, model-based RL, large-scale RL).
• Hands‑on deployment of policies on physical robots.
• Strong engineering skills in Python + PyTorch/JAX and scalable training systems.
• Experience with humanoids, multi-contact control, whole-body planning, or balance control.
• Experience with distributed GPU training and sim-to-real pipelines.
• Publications at NeurIPS/ICLR/RSS/CoRL/CVPR.
• Familiarity with ROS2, unitree-sdk, LCM, or real-time control loops.

Why Join Us

• Collaborate with top researchers and professors across AI, physics, and robotics.
• Competitive compensation, equity, and large-scale compute.