Reinforcement Learning Research Engineer

About the Position

Join our Deep Learning Lab as a Reinforcement Learning Research Engineer to develop and evaluate robust and reproducible learned policies for sequential decisions under uncertainty.

Responsibilities

• Translate a control problem into environments, rewards, and measurable baselines.
• Run policy-training experiments with seed sweeps and held-out scenarios.
• Study reward exploitation, distribution shifts, and the limits of sim-to-real transfer.

Requirements

• Hands-on experience training and debugging reinforcement learning agents.
• Strong understanding of optimization, probability, and experimental design.
• Ability to implement research ideas in Python and explain when they do not work.

Useful Evidence

• A reproducible RL experiment, paper implementation, or deployed policy.
• Robotics, offline RL, and large-scale rollout infrastructure are useful experience.

Working Terms

• Startup culture, a goal-oriented team, and a research mindset.
• The opportunity to apply your engineering skills to tools and systems for fellow engineers and help shape the future of AI.
• Latest-generation MacBook Pro.
• An in-house GPU cluster for training and experimentation.
• 20 working days of annual leave.
• English courses, educational events, and conferences.
• Medical insurance.

Tools & Systems

• PyTorch
• Isaac Lab
• MuJoCo
• Ray
• Weights & Biases

Relevant experience matters more than knowing every tool listed.

Start a Conversation

Send a CV or profile and one piece of work that helps us understand your contribution.

Location: Kyiv / remote

Experience: Senior

Working Arrangement: Arrangement to be discussed

Apply by email. Please share only work you have permission to disclose.

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