Statherós®Remote
Artificial Intelligence (AI) Engineer / Developer (Remote)
United States of America
Full Time
4 hours ago
Visa Sponsor
Key skills
Proximal Policy Optimization (PPO)Reinforcement LearningTensorFlowPyTorchJAXPythonDistributed Training SystemsPolicy Gradient MethodsOpenAI GymRLlibParallel ComputingGPU AccelerationRustAIMachine LearningOpenAICommunication
About this role
Statherós is a small DEFTECH firm focused on developing cutting-edge AI and autonomy systems for the US Department of Defense. They are seeking a talented AI Engineer specializing in Proximal Policy Optimization (PPO) to lead the development of AI-enabled algorithms that automate the operation of air traffic radar systems.
Responsibilities:
- Design, implement, and optimize Proximal Policy Optimization (PPO) algorithms for domain-specific use cases
- Develop and train reinforcement learning models for real-world applications, focusing on efficiency and scalability
- Collaborate with cross-functional teams to integrate PPO models into production systems
- Analyze model performance and experiment with hyperparameter tuning to achieve optimal results
- Stay up-to-date with the latest research and advancements in reinforcement learning and apply them to enhance existing solutions
- Build robust pipelines for training, evaluation, and deployment of RL models
- Document workflows, methodologies, and code for reproducibility and knowledge sharing
Requirements:
- Bachelor's or Master's degree in Computer Science, Machine Learning, AI, Mathematics, or related fields. Ph.D. is a plus
- 4+ years of professional experience in machine learning, with a focus on reinforcement learning
- Demonstrated expertise in implementing and optimizing PPO or similar reinforcement learning algorithms
- Hands-on experience with frameworks like TensorFlow, PyTorch, or JAX
- Strong programming skills in Python; familiarity with Rust or other languages is a plus
- Proficiency in designing and running RL experiments in simulated or real-world environments
- Experience with distributed training systems for reinforcement learning
- Solid understanding of policy gradient methods and reinforcement learning theory
- Excellent problem-solving skills and the ability to work in a collaborative, fast-paced environment
- Strong communication skills for presenting findings and collaborating with interdisciplinary teams
- Experience in applying PPO to [specific domain, e.g., robotics, gaming, finance, etc.]
- Familiarity with OpenAI Gym, RLlib, or other RL development environments
- Knowledge of parallel computing and GPU acceleration for large-scale RL tasks