AI Research Engineer - Reinforcement Learning (100% Remote)

Join Tether and shape the future of digital finance. At Tether, we’re pioneering a global financial revolution with cutting-edge solutions that empower businesses to seamlessly integrate reserve-backed tokens across blockchains.

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 behaviour to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges.

Your responsibilities include:

• Developing and implementing state-of-the-art reinforcement learning algorithms designed to optimize decision-making processes in both simulated and real-world settings.
• Establishing clear performance targets such as reward maximization and policy stability.
• Building, running, and monitoring controlled reinforcement learning experiments.
• Tracking key performance indicators while documenting iterative results and comparing outcomes against established benchmarks.
• Identifying and curating high-quality simulation environments and training datasets tailored to specific domain challenges.
• Systematically debugging and optimizing the reinforcement learning pipeline by analyzing both computational efficiency and learning performance metrics.
• Collaborating with cross-functional teams to integrate reinforcement learning agents into production systems.

Qualifications:

• A degree in Computer Science or related field; ideally a PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D.
• Proven experience with large-scale reinforcement learning experiments, including online RL techniques such as Group Relative Policy Optimization (GRPO).
• Deep understanding of reinforcement learning algorithms, including state-of-the-art online RL methods and other gradient-based optimization approaches.
• Strong expertise in PyTorch and relevant reinforcement learning frameworks.
• Practical experience in developing RL pipelines, from simulation and online training to post-training evaluation and deploying RL-based solutions in production environments.

Important information for candidates:

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