AI Research Engineer - Reinforcement Learning

Accountabilities:

• Design and implement advanced reinforcement learning algorithms to improve decision-making, policy optimization, and system performance across simulated and real-world environments.
• Run controlled experiments, track performance metrics, evaluate outcomes against benchmarks, and iterate on model improvements through empirical analysis.
• Develop and curate high-quality simulation environments and training datasets aligned with domain-specific requirements and learning objectives.
• Debug and optimize RL pipelines, addressing challenges such as exploration strategy, reward stability, sample efficiency, and training convergence.
• Collaborate with engineering and research teams to integrate RL agents into production systems and ensure measurable real-world performance gains.
• Define evaluation frameworks and continuously monitor deployed systems to support robustness, scalability, and domain adaptation.

Requirements:

• Advanced degree in Computer Science, Machine Learning, or related field; PhD preferred with strong academic research background and publications in top-tier conferences.
• Proven experience running large-scale reinforcement learning projects, including modern online RL techniques such as policy optimization methods and actor-critic frameworks.
• Deep understanding of reinforcement learning theory and practice, including policy gradients, exploration-exploitation trade-offs, and optimization strategies for stability and efficiency.
• Strong hands-on expertise with PyTorch and RL frameworks, including building full pipelines from simulation to training and deployment.
• Demonstrated ability to solve complex RL challenges such as sample inefficiency, reward noise, and training instability through empirical and algorithmic innovation.
• Strong analytical mindset with ability to design robust experiments, interpret results, and continuously improve model performance.