Artificial Intelligence (AI) Engineer / Developer (Remote)

<h4>About Us</h4><div>Statheros is a small DEFTECH firm focused on developing cutting-edge AI and autonomy systems for the US Department of Defense. Our team is passionate about building intelligent systems that solve complex problems. We are looking for a talented <strong>AI Engineer specializing in Proximal Policy Optimization (PPO)</strong> to lead the development of AI-enabled algorithms that automate the operation of air traffic radar systems.</div><hr><h4>Job Responsibilities</h4><ul><li>Design, implement, and optimize <strong>Proximal Policy Optimization (PPO)</strong> algorithms for domain-specific use cases.</li><li>Develop and train reinforcement learning models for real-world applications, focusing on efficiency and scalability.</li><li>Collaborate with cross-functional teams to integrate PPO models into production systems.</li><li>Analyze model performance and experiment with hyperparameter tuning to achieve optimal results.</li><li>Stay up-to-date with the latest research and advancements in reinforcement learning and apply them to enhance existing solutions.</li><li>Build robust pipelines for training, evaluation, and deployment of RL models.</li><li>Document workflows, methodologies, and code for reproducibility and knowledge sharing.</li></ul><hr><h4>Qualifications</h4><ul><li><strong>Educational Background</strong>: Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, Mathematics, or related fields. Ph.D. is a plus.</li><li><strong>Experience</strong>:<ul><li>4+ years of professional experience in machine learning, with a focus on reinforcement learning.</li><li>Demonstrated expertise in implementing and optimizing PPO or similar reinforcement learning algorithms.</li><li>Hands-on experience with frameworks like TensorFlow, PyTorch, or JAX.</li></ul></li><li><strong>Technical Skills</strong>:<ul><li>Strong programming skills in Python; familiarity with Rust or other languages is a plus.</li><li>Proficiency in designing and running RL experiments in simulated or real-world environments.</li><li>Experience with distributed training systems for reinforcement learning.</li><li>Solid understanding of policy gradient methods and reinforcement learning theory.</li></ul></li><li><strong>Soft Skills</strong>:<ul><li>Excellent problem-solving skills and the ability to work in a collaborative, fast-paced environment.</li><li>Strong communication skills for presenting findings and collaborating with interdisciplinary teams.</li></ul></li></ul><hr><h4>Preferred Qualifications</h4><ul><li>Experience in applying PPO to [specific domain, e.g., robotics, gaming, finance, etc.]</li><li>Familiarity with OpenAI Gym, RLlib, or other RL development environments</li><li>Knowledge of parallel computing and GPU acceleration for large-scale RL tasks</li></ul><hr><h4>What We Offer</h4><ul><li>Remote work location.</li><li>Competitive salary.</li><li>Flexible work schedule.</li><li>Opportunities for professional development and research contributions</li><li>Access to state-of-the-art resources and tools for AI development.</li><li>The chance to work on groundbreaking projects with a talented and passionate team.</li></ul>