Artificial Intelligence Engineer / Developer

About The Role: We are seeking a highly skilled AI Engineer with expertise in Proximal Policy Optimization (PPO) to join our dynamic team. The successful candidate will lead the development and implementation of reinforcement learning algorithms tailored for automating air traffic radar system operations. This role involves designing, training, and optimizing PPO-based models to improve system automation and performance in real-world defense scenarios. The AI Engineer will collaborate closely with cross-disciplinary teams to integrate these models into operational environments, ensuring robustness and scalability. The ideal candidate will possess a strong background in machine learning, particularly reinforcement learning, and have experience deploying these models in complex, high-stakes environments. This position offers the opportunity to work on groundbreaking projects that directly contribute to national security and defense capabilities, utilizing state-of-the-art AI technologies.

Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or related fields; Ph.D. is a plus. Minimum of 4+ years of professional experience in machine learning, with a focus on reinforcement learning. Proven expertise in implementing and optimizing Proximal Policy Optimization (PPO) or similar reinforcement learning algorithms. Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or JAX.Strong programming skills in Python; familiarity with Rust or other languages is advantageous. Experience designing and executing reinforcement learning experiments in simulated and real-world environments. Knowledge of distributed training systems for reinforcement learning. Solid understanding of policy gradient methods and reinforcement learning theory. Excellent problem-solving skills and ability to work effectively in a collaborative, fast-paced environment. Strong communication skills for presenting complex technical findings to interdisciplinary teams.

Responsibilities: Design, implement, and optimize Proximal Policy Optimization (PPO) algorithms for specific domain applications. Develop, train, and evaluate reinforcement learning models aimed at automating air traffic radar system operations. Collaborate with software engineers, data scientists, and domain experts to integrate RL models into production systems. Analyze model performance through rigorous testing and hyperparameter tuning to ensure optimal results. Stay current with the latest research and advancements in reinforcement learning, applying new techniques to enhance existing solutions. Build and maintain robust pipelines for training, evaluation, and deployment of reinforcement learning models. Document workflows, methodologies, and codebases to ensure reproducibility and facilitate knowledge sharing within the team. Participate in peer reviews, knowledge sharing sessions, and contribute to the continuous improvement of AI development processes.

Benefits: Remote work flexibility, allowing for a balanced work environment. Competitive salary package commensurate with experience and expertise. Flexible work hours to accommodate personal and professional commitments. Opportunities for ongoing professional development and participation in cutting-edge research projects. Access to advanced AI tools, resources, and state-of-the-art infrastructure. Engagement in impactful projects that enhance national security and defense capabilities. Supportive team environment that fosters innovation and growth.

Equal Opportunity: Statheros is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, ethnicity, gender, age, sexual orientation, disability, or any other protected characteristic. All qualified applicants will receive consideration for employment without regard to these factors, and we encourage individuals from diverse backgrounds to apply.