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

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

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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