Reinforcement Learning AI Engineer

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.

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.

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
• Secret clearance
• Bachelor's degree in a CS, AI, or Engineering field

Preferred Qualifications

• 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
• TS/SCI clearance
• Master’s degree in CS, AI, Engineering, or a related field

Benefits

• Health, life, disability, financial, and retirement benefits
• Paid leave
• Professional development and tuition assistance
• Work-life programs and dependent care
• Recognition awards program for exceptional performance and superior demonstration of company values

Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits.

Pay

Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $99,000.00 to $225,000.00 (annualized USD).

Schedule

This posting will close within 90 days from the posting date.

Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.

• Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
• Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
• Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.