Design, implement, and train reinforcement learning (RL) and multi-agent reinforcement learning (MARL) algorithms for complex decision-making problems.
Develop scalable training pipelines using Python and modern ML frameworks.
Build and evaluate agents in simulated environments using Gym or PettingZoo, high-fidelity simulators, or custom environments.
Apply RL techniques such as policy optimization, value-based learning, model-based RL, and imitation learning.
Collaborate with domain experts to define reward structures, constraints, and evaluation metrics aligned with mission objectives.
Implement distributed training workflows leveraging cloud compute, containerization, and orchestration technologies.
Transition trained models into production systems, following strong software engineering best practices.
Contribute to system architecture and performance optimization in Python with opportunities to extend into C++ or Rust for high-performance components.

Requirements

Experience developing and training reinforcement learning agents
Experience with Gym or PettingZoo interfaces
Experience with ML frameworks such as PyTorch, TensorFlow, or JAX
Experience with artificial intelligence, data science, machine learning engineering, or software engineering
Experience developing technical solutions using Python, C++, or Rust
Knowledge of reinforcement learning and artificial neural networks
Ability to obtain a Secret clearance
Bachelor's degree in a Computer Science, Artificial Intelligence, or Engineering field

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

reinforcement learningmulti-agent reinforcement learningPythonGymPettingZoopolicy optimizationvalue-based learningmodel-based RLimitation learningC++

Certifications

Secret clearance