Reinforcement Learning AI Engineer

Booz Allen Hamilton•Huntsville, AL

About The Position

Are you an innovative and experienced artificial intelligence (AI) developer specializing in reinforcement learning? Utilize your expertise in AI, data science, and machine learning (ML) engineering to train, test, deploy, and maintain models that learn from data to drive real-world mission-critical impact. You will collaborate with dynamic teams to translate reinforcement learning research into operational capability and production-grade code, bringing significant technological advancements that drive mission success. You’ll join a growing community of ML engineers focused on delivering products and solutions to our customers’ most challenging problems. You’ll collaborate with a team of dedicated space, military, intelligence, engineering, and AI professionals to deliver cutting-edge solutions to solve issues of national importance.

Requirements

3+ years of experience developing and training RL agents
Experience with AI, data science, ML engineering, or software engineering
Experience with Gym or PettingZoo interfaces
Experience with ML frameworks such as PyTorch, TensorFlow, or JAX
Experience developing technical solutions using Rust, Python, or C++
Knowledge of RL and artificial neural networks
Ability to travel up to 25% of the time
Secret clearance
Bachelor's degree in a CS, AI, or Engineering field

Nice To Haves

Experience applying RL to autonomy, control systems, or mission-scale
Experience with MARL
Experience with AFSIM or other high-fidelity simulation environments
Experience with embedded systems programming in Rust, C, or C++
Experience in GPU programming, including CUDA or RAPID
Experience developing in-space solutions
Knowledge of modern software design patterns, including microservice design and orchestration in Kubernetes deployment
Master’s degree in CS, AI, Engineering, or a related field

Responsibilities

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.