Reinforcement Learning AI Engineer

Booz Allen Hamilton•Aurora, CO

5d•Onsite

About The Position

Booz Allen is seeking an innovative and experienced AI developer specializing in reinforcement learning to join our growing team for Space solutions. In this role, you will leverage your expertise in artificial intelligence, data science, and machine learning engineering to train, test, deploy, and maintain models that learn from data. You will collaborate with cross-functional teams to translate reinforcement learning research into operational capability and production-grade code, bringing significant technological advancements that drive mission success. You’ll pioneer a growing community of machine learning engineers across the company. You’ll collaborate with a team of dedicated Space, Military, Intelligence, Engineering, and AI professionals to deliver bleeding-edge solutions to solve high-priority national defense problems.

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
Secret clearance
Bachelor's degree in a Computer Science, Artificial Intelligence, or Engineering field

Nice To Haves

Experience applying RL to autonomy, control systems, or mission-scale
Experience with Multi-Agent Reinforcement Learning (MARL)
Experience with AFSIM or other high-fidelity simulation environments
Experience with embedded systems programming in C, C++, or Rust
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 Computer Science, Artificial Intelligence, 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.