Defense AI/RL Engineer for Simulation & Training

Torch Technologies

Huntsville (AL)

On-site

USD 90,000 - 130,000

Full time

7 days ago

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Benefits offered by this job

ESOP participation

401(k) match

Medical

Dental

Vision

Life insurance

Short-term disability

Long-term disability

Flexible spending accounts

Health Saving Accounts

Employee Assistance Program

Education assistance

Paid time off

Holidays

Job summary

Torch Technologies, Inc. seeks an Artificial Intelligence / Reinforcement Learning Engineer to research, develop, implement, and evaluate advanced RL/ML solutions for defense modeling, simulation, and decision-support applications.

Strong Python and MATLAB experience and Ray RLlib usage are required. The ideal candidate will design reward functions, observation/action representations, and integrate AI/ML with C++ or other languages in simulation environments, supporting engineering teams and

Qualifications

US Citizenship.
Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Mathematics, or a related technical discipline.
3+ years of relevant professional experience in software engineering, machine learning, reinforcement learning, or numerical algorithm development.
Strong Python programming skills, including experience with scientific computing and AI/ML software.
Experience developing or evaluating reinforcement learning algorithms using Ray RLlib.
Experience using MATLAB for algorithm development, data analysis, simulation, or mathematical modeling.
Understanding of fundamental reinforcement learning concepts, including policies, rewards, observations, actions, and agent–environment interactions.
Experience with machine learning frameworks such as PyTorch, TensorFlow, or comparable libraries.
Strong analytical, debugging, and problem‑solving skills.
Ability to communicate technical findings through written documentation and engineering discussions.

Responsibilities

Research, develop, and evaluate reinforcement learning algorithms for complex decision-making and optimization problems.
Implement and train reinforcement learning agents using Python and Ray RLlib.
Develop and evaluate algorithms such as PPO, SAC, and other applicable reinforcement learning approaches.
Design observation spaces, action spaces, reward functions, and episode termination conditions.
Evaluate training stability, convergence, generalization, and algorithm performance under varying environmental conditions.
Perform hyperparameter tuning, comparative algorithm evaluations, and controlled experimentation.
Analyze experimental results and recommend improvements to model architectures, training configurations, and reward strategies.
Develop and maintain Python-based reinforcement learning software, numerical algorithms, and experimentation utilities.
Use MATLAB to conduct mathematical analysis, algorithm prototyping, performance evaluation, and visualization of experimental results.
Translate research concepts, mathematical models, and analytical prototypes into maintainable software implementations.
Develop scripts and utilities for processing simulation data, analyzing performance metrics, and visualizing results.
Apply software engineering best practices, including code reviews, testing, version control, and technical documentation.
Develop configurable training pipelines supporting repeatable experiments.
Use distributed computing and GPU resources to improve training efficiency.
Collect and analyze performance metrics from training and evaluation experiments.
Document experimental methods, results, limitations, and technical findings.
Support the transition of successful research concepts into operationally relevant software prototypes.
Develop and integrate reinforcement learning environments using Gymnasium or comparable frameworks.
Connect reinforcement learning agents with modeling and simulation systems for training and evaluation.
Support integration between Python-based AI/ML applications and simulation software developed in C++ or other programming languages.
Develop reproducible training and evaluation workflows for simulation-based experiments.
Collaborate with simulation engineers and domain experts to ensure that environment models accurately represent the intended research objectives.