AI Research Engineer - Reinforcement Learning

This role sits at the forefront of applied AI research, focusing on advancing reinforcement learning systems that power next-generation intelligent models. You will design and optimize algorithms that improve decision-making, adaptability, and performance across complex, real-world environments. Working in a highly research-driven and experimentation-heavy setting, you will contribute to both foundational RL innovations and production-grade implementations. The position spans work on efficient models for constrained hardware as well as large-scale multimodal systems integrating text, image, and audio. You will play a key role in building simulation environments, refining training pipelines, and enhancing policy performance. This is an opportunity to directly shape cutting-edge AI systems deployed at global scale.

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

Benefits

• Fully remote work environment with global team collaboration
• Opportunity to work on cutting‑edge AI and reinforcement learning research at scale
• High‑impact role influencing production‑level AI systems and real‑world applications
• Competitive compensation aligned with experience and expertise
• Exposure to advanced research, multimodal AI systems, and state‑of‑the‑art infrastructure
• Flexible working culture supporting autonomy and innovation