As a member of the AI model team, you will drive innovation in reinforcement learning approaches for advanced models.
Your work will optimize decision-making and adaptive behavior to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges.
You will work across a broad spectrum of systems, including resource-efficient models designed for limited hardware environments and complex multi-modal architectures that integrate data such as text, images, and audio.
We expect you to have deep expertise in designing reinforcement learning systems and a strong background in advanced model architectures.
You will adopt a hands-on, research-driven approach to developing, testing, and implementing novel reinforcement learning algorithms and training frameworks.
Your responsibilities include curating specialized simulation environments and training datasets, strengthening baseline policy performance, and identifying as well as resolving bottlenecks in the reinforcement learning process.

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