Artificial Intelligence/Reinforcement Learning Engineer

Torch Technologies Thank you for your interest in employment with Torch Technologies. We are a 100% employee-owned, Certified Great Place To Work and named Best Places to Work in Huntsville/Madison County, headquartered in Huntsville, AL. Our team provides superior research, development, and engineering services to the Federal Government and Department of War. As one of the nation’s top 100 defense companies, the services we provide directly support the men and women who serve our country. Our corporate mission sums up the pride our employee-owners take in the work we do: “Lighting the Pathway of Freedom”. And, as a Certified Evergreen ESOP, we have made the commitment to grow and sustain our company for the next 100 years! Come grow with us! Torch Technologies Inc., is seeking an Artificial Intelligence / Reinforcement Learning (AI/RL) Engineer to research, develop, implement, and evaluate advanced reinforcement learning and machine learning solutions supporting defense modeling, simulation, and decision-support applications. The ideal candidate will have strong programming experience in Python and MATLAB, along with practical experience using Ray RLlib to develop, train, and evaluate reinforcement learning algorithms. This position focuses on applying reinforcement learning techniques to complex decision-making problems, developing simulation-based training environments, designing reward functions and observation/action representations, and evaluating algorithm performance. 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 #LI-TS1 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. Torch Technologies is dedicated to providing superior research, development, and engineering services to the Department of Defense. As defense contractors, the services we provide directly support the men and women who serve our country. From the CEO's desk... When Bill Roark and Don Holder founded Torch in 2002, they knew that employees were the company's greatest asset. They believed that offering employees a stake in the outcome, by making them all owners, was critical to building a successful organization. I share that feeling. Bill and Don's original intent was fully achieved in 2011 when Torch converted to a 100 percent S-Corp ESOP. Since that time, the company has grown over ten-fold and gained national recognition for the success we have demonstrated as an employee-owned company. Today, Torch continues to respect the individual contributions of our employee-owners and to share the rewards of our success. In addition, we continue to foster an environment of employee recognition, respect, development, and comradery while striving to achieve a healthy work-life balance. We recognize that core to our company is a culture of trust with our employees, customers, business partners, and community. Our management team is committed to continue to earn that trust. This past year, Torch proudly announced that we were Certified Evergreen™. A timely achievement for Torch employee-owners as they will celebrate Torch’s 20th anniversary this October, becoming Certified Evergreen™ signifies that while Torch employee-owners can look back on Torch’s past with pride, Torch is also looking forward. Becoming Certified Evergreen™ means that Torch has committed to finding ways to make 100 percent employee ownership sustainable so that Torch will remain a private company forever. Going forward, Torch will continue to evolve to address market and technology changes in order to become a major provider of cost-effective technical services and solutions to the Warfighter. We are making significant investments into facilities, equipment, business systems, and research to enable us to provide innovations and timely business services to our clients. We feel that always looking to improve our service is key to sustaining customer trust in the future. Likewise, we believe that continuing to share ownership is one of the ways we maintain employee trust and an unparalleled commitment to succeed. Combined, these timeless principles form the basis of the Torch culture. A culture that we are committed to continue and strive to pass on to the next generation of employee-owners as an Evergreen Company.