<- Open Roles

Open Role

Research Scientist: Reinforcement Learning

Singapore / Munich, Germany

Fine-tune VLA, WAM, and other multimodal models with reinforcement learning to improve robot task success, robustness, and generalization.

About the Role

MindOn is building general-purpose intelligence for robots. We are looking for a Research Scientist focused on reinforcement learning to develop reinforcement learning post-training and optimization for robot manipulation models.

You will build on pretrained Vision-Language-Action (VLA), World Action Models (WAM), and other multimodal models, using task feedback, interaction data, and reward signals to improve reliable task completion in real environments.

Responsibilities

  • Develop reinforcement learning fine-tuning and post-training methods for VLA, WAM, and other multimodal models to improve task success rates and execution stability.
  • Design task rewards, success criteria, and evaluation metrics, addressing sparse rewards, long-horizon tasks, and reward bias.
  • Combine demonstrations, offline data, and online interactions into effective training workflows for robot manipulation.
  • Build iterative interaction data collection, policy update, and evaluation pipelines to reduce real-robot interaction costs and continuously improve performance.
  • Analyze failures, distribution shift, and training instability while limiting degradation of pretrained capabilities.
  • Collaborate with robot control, software, and data teams on deployment, interaction data collection, and real-robot validation.
  • Build reproducible experiments and evaluations to measure improvements in task success, robustness, and generalization.

Qualifications

  • A deep and genuine passion for Embodied AI is essential.
  • Proficiency with AI coding tools is essential: use them effectively for development, debugging, testing, and troubleshooting; independently review generated code and validate its correctness and reliability through tests and real-world execution.
  • A master’s degree or above in Computer Science, Robotics, AI, Automation, or a related field, or equivalent practical experience.
  • Familiarity with reinforcement learning fine-tuning methods and representative work for VLA and other multimodal action models, such as SimpleVLA-RL, ConRFT, and VLA-RFT; understanding of their training workflows, reward design, data usage, and applicability, with the ability to reproduce, evaluate, and improve these methods.
  • Knowledge of OpenVLA, π0, Diffusion Policy, and related models, including autoregressive, diffusion, and flow-matching action generation and corresponding approaches to reinforcement learning optimization.
  • Practical experience fine-tuning pretrained robot policies or multimodal action models with reinforcement learning, designing training plans around task success rates and validating results.
  • Familiarity with reward design, success detection, interaction data collection, and combining demonstrations with online data.
  • Ability to diagnose training instability, reward hacking, distribution shift, and degradation of pretrained capabilities.
  • Proficiency in Python and frameworks such as PyTorch, with experience developing, debugging, and optimizing training pipelines.
  • Rigorous experimental skills, including benchmark evaluation, ablations, and failure analysis to establish whether improvements are effective.
  • Strong problem-solving skills, independent execution, and cross-functional collaboration.

Preferred Experience

  • RL post-training experience with VLA, WAM, or other robot foundation models, with measurable improvements in task success.
  • Experience with online learning, policy deployment, and debugging on real robots.
  • Familiarity with human intervention data, Human-in-the-Loop learning, and combining demonstrations with reinforcement learning.
  • Experience developing visual reward models, success detection models, or learned reward functions.
  • Experience applying reinforcement learning to diffusion or flow-matching action models.
  • Familiarity with distributed training, parallel data collection, and large-scale experiment management.
Apply by email