Research Member of Technical Staff- Post-training & Robot Learning

Roboticsconnected; Mountain View (WY); On-site; USD 150,000 - 190,000; Full time

Job summary

Rhoda AI is building generalist robots and seeks researchers to adapt web-pretrained video models to real robot tasks. You will design RL training pipelines, work on policy learning, and translate video predictions into executable actions on real hardware.

We hire across senior levels, focusing on robust demonstrations and fast iteration between simulation and reality. Strong PyTorch skills and robotics expertise are essential for this role.

Qualifications

• Hands-on robotics or autonomous systems experience in industry or research.
• Strong background in policy learning and imitation learning fundamentals.
• Practical knowledge of real robot hardware and sensor modalities.

Responsibilities

• Design RL training pipelines to improve robot policy performance.
• Develop RL algorithms adapted to video prediction for robots.
• Create post-training pipelines: fine-tuning and behavioral alignment.
• Work on inverse dynamics models translating video to actions.
• Build evaluation frameworks for real hardware deployment.
• Collaborate with pre-training teams to address missing capabilities.

Skills

PyTorch

Robot systems

Reinforcement Learning

Policy learning

Video models

Education

PhD in Robotics or ML

Tools

ROS/ROS2

MuJoCo

Isaac Sim

Job description

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.

We're looking for Research Scientists and Research Engineers with deep robotics or autonomous systems domain knowledge to adapt our web-pretrained video model to real robot tasks. Post-training at Rhoda means taking a causal video generation model pretrained on internet-scale data and fine-tuning it on robot-collected demonstrations to produce reliable, generalizable behavior — with as little task-specific data as possible. We hire across levels — from senior to staff.

What You'll Do

• Design and implement RL training pipelines to improve robot policy performance beyond what imitation learning alone achieves — reward design, online data collection, and policy optimization
• Develop and apply RL algorithms (PPO, GRPO, or similar) adapted to the video prediction setting, including reward modeling and feedback collection strategies for physical task performance
• Design and implement broader post-training pipelines: supervised fine-tuning, preference optimization, and behavioral alignment on robot-collected demonstration data
• Work on the inverse dynamics model that translates video predictions into executable robot actions
• Build evaluation frameworks for post-trained policies: task success, generalization to novel objects and environments, and failure mode analysis on real hardware
• Research methods to efficiently adapt models to new tasks with minimal demonstration data, including in-context generalization and few-shot adaptation
• Identify failure modes and systematic weaknesses in deployed robot policies and drive targeted improvements
• Iterate quickly between simulation and real robot evaluation to close the feedback loop
• Collaborate with the pre-training team to surface what capabilities are missing from the base model and need to be addressed upstream

What We're Looking For

• Hands-on experience with robot systems, robotic policy learning, or autonomous systems in an industry or research setting (robotics, self-driving, or similar physical AI domains)
• Strong understanding of robot policy learning: imitation learning, behavior cloning, and how RL builds on top of it
• Practical familiarity with real robot hardware, deployment constraints, and sensor modalities (vision, proprioception)
• Solid ML skills with hands-on PyTorch experience
• Ability to diagnose policy failures, reason about distribution shift, and iterate effectively on data and training strategies
• Comfort with ambiguity and fast-changing research priorities
• Staff-level candidates are expected to define technical direction and drive research strategy independently; senior candidates execute complex projects with strong fundamentals and growing scope

Nice to Have (But Not Required)

• Hands-on experience with reinforcement learning — reward design, policy optimization, and online RL training loops — applied to real or near-real environments (robotics, games, simulated physics, or similar); this is a significant plus
• Prior industry experience in robotics, autonomous driving, or physical AI (e.g., manipulation, mobile robotics, self-driving stacks)
• Experience with teleoperation systems or robot demonstration collection at scale
• Familiarity with robot middleware (ROS/ROS2) and real-time control systems
• Experience with simulation environments for robotics (MuJoCo, Isaac Sim, Genesis)
• Understanding of video generation models and how they connect to action prediction
• PhD in Robotics, ML, or a related field
• Publication record at ICRA, CoRL, RSS, NeurIPS, or related venues

Why This Role

• Your work is what makes our robots actually perform tasks reliably in the real world — the direct connection between pre-trained capability and deployed behavior
• Work at a rare intersection: state-of-the-art video generation models applied to real robot hardware, not simulation
• Fast feedback loop between model changes and real robot performance
• High ownership on a small team where robotics domain expertise is core to the mission