AI Research Engineer - Reinforcement Learning

Overview

Accountabilities

• Design, develop, and implement advanced reinforcement learning algorithms to optimize decision-making processes across simulated and real-world environments.
• Build, execute, monitor, and evaluate large-scale reinforcement learning experiments while tracking key performance indicators and benchmark results.
• Develop and curate high-quality simulation environments and training datasets tailored to domain-specific reinforcement learning challenges.
• Optimize reinforcement learning pipelines by identifying and resolving issues related to exploration strategies, policy divergence, reward signal instability, and computational efficiency.
• Improve policy performance, convergence stability, and sample efficiency through advanced optimization techniques and iterative experimentation.
• Collaborate with engineering and research teams to integrate reinforcement learning agents into production systems and real-world applications.
• Define measurable success metrics and continuously monitor deployed RL systems to ensure robustness, scalability, and sustained performance improvements.
• Contribute to ongoing AI research initiatives by exploring innovative RL methodologies, model architectures, and training frameworks.
• Document experimental findings, technical approaches, and research outcomes to support knowledge sharing and continuous innovation.

Requirements

• Degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field; PhD preferred.
• Strong research background in reinforcement learning, machine learning, NLP, or AI-related disciplines with proven contributions to advanced AI research initiatives.
• Hands-on experience conducting large-scale reinforcement learning experiments, including online RL methods such as Group Relative Policy Optimization (GRPO).
• Deep understanding of reinforcement learning concepts including policy gradients, actor-critic methods, GRPO, exploration-exploitation tradeoffs, and policy optimization techniques.
• Strong expertise in PyTorch and reinforcement learning frameworks, including experience building end-to-end RL pipelines.
• Experience developing, training, evaluating, and deploying reinforcement learning systems in production or large-scale research environments.
• Proven ability to solve complex RL challenges such as sample inefficiency, training instability, reward optimization, and convergence issues.
• Experience working with multi-modal AI systems and resource-efficient model architectures is considered a strong advantage.
• Strong analytical, problem-solving, and experimentation skills with a research-driven mindset.
• Excellent communication and collaboration abilities within distributed and cross-functional teams.

Benefits

• Fully remote work environment with global collaboration opportunities.
• Opportunity to work on cutting-edge AI and reinforcement learning technologies.
• Exposure to advanced multi-modal architectures and large-scale AI research initiatives.
• Flexible and innovation-focused work culture that encourages experimentation and continuous learning.
• Collaboration with highly skilled international AI researchers and engineers.
• Opportunity to contribute to impactful AI systems with real-world applications.
• Career growth opportunities within a rapidly evolving global technology environment.
• Dynamic and fast-paced setting focused on innovation, research excellence, and technical ownership.

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