The selected candidate will work with software engineers, simulation developers, and subject matter experts to research and implement AI/ML solutions for military applications.
Key Responsibilities
Reinforcement Learning Research and Development
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 Proximal Policy Optimization (PPO), Soft Actor-Critic (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.
Python and MATLAB Development
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.
Modeling & Simulation Integration
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.
AI/ML Experimentation
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.
Key Technical Priorities
Python: Strong software development experience implementing AI/ML algorithms, data processing, and experimental workflows.
Ray RLlib: Hands-on experience configuring, training, evaluating, and tuning reinforcement learning algorithms.
MATLAB: Experience with numerical analysis, algorithm development, modeling, data processing, and research prototyping.
Reinforcement Learning: Understanding of RL algorithms, policy optimization, exploration strategies, reward design, and agent-environment interactions.
Modeling & Simulation: Experience developing or integrating AI algorithms with simulation environments.
Minimum Qualifications
US Citizenship.
Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Mathematics, or a related technical discipline.
3 or more 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.
Preferred Qualifications
Master's degree or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related field.
Experience with continuous observation and action spaces and algorithms such as PPO and SAC.
Experience with Gymnasium, Ray Tune, or other AI/ML experimentation and optimization frameworks.
Experience developing custom reinforcement learning environments.
Experience working with defense modeling and simulation tools such as AFSIM or OneSAF.
Experience with time-series analysis, optimization, or numerical computing using MATLAB.
Familiarity with C++ and Python/C++ interoperability.
Experience using Linux, GPU-accelerated training, and distributed computing.
Experience with Singularity/Apptainer or other container technologies.
Experience conducting applied research for military, aerospace, or defense applications.
Familiarity with battle management, sensor management, engagement planning, or tactical decision-support problems.
Desired Technical Skills
Primary: Python, Ray RLlib, MATLAB, Reinforcement Learning
AI/ML: PyTorch, PPO, SAC, Neural Networks, Hyperparameter Optimization, Experiment Evaluation
Simulation: Gymnasium, Custom RL Environments, AFSIM, OneSAF
Software Development: Git, Linux, C++, Automated Testing, Scientific Computing
Computing: GPU Acceleration, Distributed Training, Ray Tune, Singularity/Apptainer
Position Impact
This position will contribute directly to the development of advanced reinforcement learning technologies for defense research and applications. The successful candidate will develop, evaluate, and improve AI-based decision-making algorithms while helping integrate research concepts into simulation-driven experimentation environments. The role emphasizes hands-on research, software engineering, experimentation, and algorithm development rather than personnel management.
Schedule: M-F; 8-5
Work Location: Customer-site
Travel: Yes, up to 10%
Relocation Assistance Available: No
Position Contingent Upon Award of Contract: No
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Benefits
Torch Technologies is proud to offer a stable and professional work environment, a competitive salary, and an excellent, comprehensive benefit package including: ESOP participation, 401(k) match, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, Health Saving Accounts and Health Reimbursement Accounts, EAP, education assistance, paid time off, and holidays.
Applying to Torch Technologies
Only those candidates invited for an interview will be contacted. Employment at Torch Technologies is contingent upon the successful completion of a comprehensive background check.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, citizenship, ancestry, marital status, protected veteran status, disability status or any other status protected by federal, state, or local law. Torch Technologies, Inc. participates in E-Verify.
If you are a qualified individual with a disability or a disabled veteran, you have the right to request a reasonable accommodation if you are unable or limited in your ability to use or access Careers Link as a result of your disability. You can request reasonable accommodations by sending an email to HR@torchtechnologies.com. Thank you for your interest in Torch Technologies.
