Develop and implement state-of-the-art reinforcement learning algorithms designed to optimize decision-making processes in both simulated and real-world settings.
Establish clear performance targets such as reward maximization and policy stability.
Build, run, and monitor controlled reinforcement learning experiments.
Track key performance indicators while documenting iterative results and comparing outcomes against established benchmarks.
Identify and curate high-quality simulation environments and training datasets that are tailored to specific domain challenges.
Set measurable criteria to ensure that the selection and preparation of these resources significantly enhance the learning process and overall model performance.
Systematically debug and optimize the reinforcement learning pipeline by analyzing both computational efficiency and learning performance metrics.
Address issues such as reward signal noise, exploration strategy, and policy divergence to improve convergence and stability.
Collaborate with cross-functional teams to integrate reinforcement learning agents into production systems.
Define clear success metrics such as real-world performance improvements and robustness under varied conditions and ensure continuous monitoring and iterative refinements for sustained domain adaptation.

A degree in Computer Science or related field.
Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).
Proven experience with large-scale reinforcement learning experiments, including online RL techniques such as Group Relative Policy Optimization (GRPO), is essential.
Your contributions should have led to measurable improvements in domain-specific decision-making and overall policy performance.
Deep understanding of reinforcement learning algorithms is required, including state-of-the-art online RL methods and other gradient-based optimization approaches like policy gradients, actor-critic, and GRPO.
Your expertise should emphasize enhancing policy stability, exploration, and sample efficiency in complex, dynamic environments.
Strong expertise in PyTorch and relevant reinforcement learning frameworks is a must.
Practical experience in developing RL pipelines, from simulation and online training to post-training evaluation and deploying RL-based solutions in production environments is expected.
Demonstrated ability to apply empirical research to overcome reinforcement learning challenges such as sample inefficiency, exploration-exploitation tradeoffs, and training instability.
You should be proficient in designing robust evaluation frameworks and iterating on algorithmic innovations to continuously push the boundaries of RL agent performance.

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AI Research Engineer – Reinforcement Learning

Key skills