# Research Scientist, Reinforcement Learning · Good AI Labs

> Advance how foundational models learn through interaction, with reinforcement learning and simulation for robotics.

URL: https://www.goodailabs.com/careers/research-scientist-world-action-models/
Date: 2026-10-13
Last updated: 2026-10-13
Work arrangement: Remote-first


Related work: [Research at Good AI Labs](https://www.goodailabs.com/research/)

## Applying

Email your application to research@goodailabs.com, with the role title in the subject line. Include your location and availability. Please share only material you have permission to disclose.

Email: research@goodailabs.com. Include the role title in the subject line.


We are hiring a Research Scientist to develop reinforcement-learning methods for embodied intelligence. The objective is to help foundational models acquire new capabilities through interaction, improve with feedback and adapt their behaviour when a task changes.

You will work across the learning algorithm, reward signal, training environment and resulting policy. Simulation is central to this role: it provides a way to study exploration, scale experience collection and test behaviour before physical evaluation. We are looking for someone who can turn an open research question into a training system and explain what the learned policy is actually doing.

## Responsibilities

- Develop reinforcement-learning algorithms and training objectives for policies that must learn from demonstrations, feedback and interaction.
- Investigate reward design, exploration, credit assignment and curricula for tasks that require sustained sequences of actions.
- Build experiments that connect pretrained foundational models to policy learning and adaptation.
- Use simulation to study sample efficiency, robustness and transfer, including the effects of environment design and training distributions.
- Diagnose training instability, reward exploitation and policy failures through targeted experiments and trajectory analysis.
- Collaborate with robotics and systems engineers on experience collection, distributed training and evaluation on physical tasks.

## Qualifications

- BS, MS or PhD in machine learning, robotics, computer science or a related quantitative field, or demonstrated work of equivalent technical depth.
- Original reinforcement-learning research or substantial learning systems, with hands-on responsibility for training policies and evaluating their behaviour.
- A research record demonstrated by leading contributions at venues such as NeurIPS, ICML, ICLR, CoRL or RSS, or research systems that establish comparable algorithmic and experimental depth.
- Practical experience implementing RL algorithms, managing rollout collection and debugging optimisation, rewards and environment behaviour.
- Strong Python and PyTorch skills, supported by a sound understanding of probability, optimisation and sequential decision-making.
- The ability to design controlled evaluations and distinguish a policy improvement from an easier environment, altered reward or change in available data.

## Preferred experience {#experience-that-would-help}

- Offline or online RL, imitation learning, policy optimisation or learning from human feedback.
- Physics simulation, manipulation, domain randomisation or transfer between simulated and physical systems.
- Distributed rollout systems, curriculum learning or adaptation of pretrained models through reinforcement learning.

## Application materials {#what-to-include-in-your-application}

Include your CV and selected research or engineering contributions. Describe an RL training project you personally developed: the environment, learning signal, policy, principal failure modes and final result. Include code, publications or rollout demonstrations where available, and explain the next problem you would want to investigate.

Contact: research@goodailabs.com