Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Mathematics, or related; Ph.D. is a plus.
• 4+ years of machine learning experience, focusing specifically on reinforcement learning.
• Expertise in implementing and optimizing Proximal Policy Optimization algorithms.
• Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or JAX.
• Strong Python programming skills; familiarity with Rust is a plus.
• Proficient in conducting RL experiments in simulated or real-world settings.
• Solid grasp of policy gradient methods and reinforcement learning theory.

Responsibilities

• Design, implement, and optimize Proximal Policy Optimization (PPO) algorithms for specific applications.
• Develop and train reinforcement learning models with an emphasis on efficiency and scalability.
• Collaborate with teams to integrate PPO models into production systems.
• Analyze model performance and tune hyperparameters for best results.
• Stay current on research in reinforcement learning to enhance existing solutions.
• Build robust pipelines for training and deploying RL models.
• Document workflows and methodologies for knowledge sharing and reproducibility.

Benefits

• Remote work location.
• Flexible work schedule.
• Opportunities for professional development and research contributions.
• Access to state-of-the-art resources and tools for AI development.
• The opportunity to work on groundbreaking projects with a talented and passionate team.