Tether: AI Research Engineer

About the Role

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.

• Develop and implement state-of-the-art reinforcement learning algorithms designed to optimize decision-making processes in simulated and real-world settings.
• Build, run, and monitor controlled reinforcement learning experiments, tracking key performance indicators and documenting iterative results.
• Identify and curate high-quality simulation environments and training datasets tailored to specific domain challenges.
• Systematically debug and optimize the reinforcement learning pipeline by analyzing computational efficiency and learning performance metrics.
• Collaborate with cross-functional teams to integrate reinforcement learning agents into production systems and ensure continuous monitoring.

• A degree in Computer Science or a related field; ideally a PhD in NLP, Machine Learning, or a related field with a 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 policy gradients, actor-critic, and other gradient-based optimization approaches.
• Strong expertise in PyTorch and relevant reinforcement learning frameworks.
• Demonstrated ability to apply empirical research to overcome challenges like sample inefficiency and training instability.

Required Skills