{"id":3622472,"url":"https://alion.io/job/starfish-holdings-artificial-intelligencereinforcement-learning-engineer","title":"Artificial Intelligence/Reinforcement Learning Engineer","company":{"id":5995,"name":"Starfish Holdings","domain":"starfish.io","url":"https://alion.io/company/starfish","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":77,"open_postings":27,"ghost_share":0,"stale_share":0.926,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redstone Arsenal, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":true,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Gymnasium","optional":false},{"name":"Machine Learning","optional":false},{"name":"MATLAB","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLlib","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"AFSIM","optional":true},{"name":"Git","optional":true},{"name":"Linux","optional":true},{"name":"Ray Tune","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-11-02T18:51:43Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-08T15:43:03Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Torch Technologies\nThank 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!\nTorch 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.\nThis 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.\nThe selected candidate will work with software engineers, simulation developers, and subject matter experts to research and implement AI/ML solutions for military applications.\nKey Responsibilities:\nReinforcement Learning Research and Development\nResearch, develop, and evaluate reinforcement learning algorithms for complex decision-making and optimization problems.\nImplement and train reinforcement learning agents using Python and Ray RLlib.\nDevelop and evaluate algorithms such as Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and other applicable reinforcement learning approaches.\nDesign observation spaces, action spaces, reward functions, and episode termination conditions.\nEvaluate training stability, convergence, generalization, and algorithm performance under varying environmental conditions.\nPerform hyperparameter tuning, comparative algorithm evaluations, and controlled experimentation.\nAnalyze experimental results and recommend improvements to model architectures, training configurations, and reward strategies.\nPython and MATLAB Development\nDevelop and maintain Python-based reinforcement learning software, numerical algorithms, and experimentation utilities.\nUse MATLAB to conduct mathematical analysis, algorithm prototyping, performance evaluation, and visualization of experimental results.\nTranslate research concepts, mathematical models, and analytical prototypes into maintainable software implementations.\nDevelop scripts and utilities for processing simulation data, analyzing performance metrics, and visualizing results.\nApply software engineering best practices, including code reviews, testing, version control, and technical documentation.\nModeling & Simulation Integration\nDevelop and integrate reinforcement learning environments using Gymnasium or comparable frameworks.\nConnect reinforcement learning agents with modeling and simulation systems for training and evaluation.\nSupport integration between Python-based AI/ML applications and simulation software developed in C++ or other programming languages.\nDevelop reproducible training and evaluation workflows for simulation-based experiments.\nCollaborate with simulation engineers and domain experts to ensure that environment models accurately represent the intended research objectives.\nAI/ML Experimentation\nDevelop configurable training pipelines supporting repeatable experiments.\nUse distributed computing and GPU resources to improve training efficiency.\nCollect and analyze performance metrics from training and evaluation experiments.\nDocument experimental methods, results, limitations, and technical findings.\nSupport the transition of successful research concepts into operationally relevant software prototypes.\nKey Technical Priorities:\nPython: Strong software development experience implementing AI/ML algorithms, data processing, and experimental workflows.\nRay RLlib: Hands-on experience configuring, training, evaluating, and tuning reinforcement learning algorithms.\nMATLAB: Experience with numerical analysis, algorithm development, modeling, data processing, and research prototyping.\nReinforcement Learning: Understanding of RL algorithms, policy optimization, exploration strategies, reward design, and agent-environment interactions.\nModeling & Simulation: Experience developing or integrating AI algorithms with simulation environments.\nMinimum Qualifications:\nUS Citizenship.\nBachelor's degree in Computer Science, Computer Engineering, Software Engineering, Mathematics, or a related technical discipline.\n3 or more years of relevant professional experience in software engineering, machine learning, reinforcement learning, or numerical algorithm development.\nStrong Python programming skills, including experience with scientific computing and AI/ML software.\nExperience developing or evaluating reinforcement learning algorithms using Ray RLlib.\nExperience using MATLAB for algorithm development, data analysis, simulation, or mathematical modeling.\nUnderstanding of fundamental reinforcement learning concepts, including policies, rewards, observations, actions, and agent-environment interactions.\nExperience with machine learning frameworks such as PyTorch, TensorFlow, or comparable libraries.\nStrong analytical, debugging, and problem-solving skills.\nAbility to communicate technical findings through written documentation and engineering discussions.\nPreferred Qualifications:\nMaster's degree or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related field.\nExperience with continuous observation and action spaces and algorithms such as PPO and SAC.\nExperience with Gymnasium, Ray Tune, or other AI/ML experimentation and optimization frameworks.\nExperience developing custom reinforcement learning environments.\nExperience working with defense modeling and simulation tools such as AFSIM or OneSAF.\nExperience with time-series analysis, optimization, or numerical computing using MATLAB.\nFamiliarity with C++ and Python/C++ interoperability.\nExperience using Linux, GPU-accelerated training, and distributed computing.\nExperience with Singularity/Apptainer or other container technologies.\nExperience conducting applied research for military, aerospace, or defense applications.\nFamiliarity with battle management, sensor management, engagement planning, or tactical decision-support problems.\nDesired Technical Skills:\nPrimary: Python, Ray RLlib, MATLAB, Reinforcement Learning\nAI/ML: PyTorch, PPO, SAC, Neural Networks, Hyperparameter Optimization, Experiment Evaluation\nSimulation: Gymnasium, Custom RL Environments, AFSIM, OneSAF\nSoftware Development: Git, Linux, C++, Automated Testing, Scientific Computing\nComputing: GPU Acceleration, Distributed Training, Ray Tune, Singularity/Apptainer\nPosition Impact:\nThis 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.\nSchedule: M-F; 8-5\nWork Location: Customer-site\nTravel: Yes, up to 10%\nRelocation Assistance Available: No\nPosition Contingent Upon Award of Contract: No\n#LI-TS1\nBenefits:\nTorch 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.\nApplying to Torch Technologies:\nOnly those candidates invited for an interview will be contacted. Employment at Torch Technologies is contingent upon the successful completion of a comprehensive background check.\n­\nAll 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.\nIf 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. 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