Senior Reinforcement Learning Engineer – Bio-Defense & Complex Systems - (US-based only)
We're seeking a Senior Reinforcement Learning Engineer to join an advanced AI-driven technology company solving high-impact, real-world problems in healthcare, insurance, and complex system modeling. This role focuses on designing, implementing, and deploying RL-based decision-making and adaptive control systems in critical bio-defense, claims resilience, and risk-sensitive environments.
Role Overview :
As a Senior RL Engineer, you'll work at the intersection of reinforcement learning, simulation, and applied machine learning. You will translate theoretical RL and systems models into operational, production-ready solutions with measurable real-world impact. This is a hands-on role, with autonomy to drive experimentation, training, validation, and deployment of RL and multi-agent systems.
Key Responsibilities :
- Design, implement, and optimize RL agents for complex, dynamic, and high-stakes environments
- Develop simulation environments (stochastic, agent-based, or hybrid) to train and evaluate RL policies
- Integrate RL models with supervised and unsupervised ML pipelines using structured (tabular) and temporal data
- Evaluate model robustness, generalization, and failure modes under uncertainty or adversarial conditions
- Collaborate with domain experts to formalize reward functions, constraints, and state spaces
- Maintain hands-on involvement in experimentation, deployment, and optimization
Required Qualifications :
4+ years of ML experience, with deep expertise in reinforcement learningStrong foundation in MDPs, POMDPs, policy gradients, value-based methods, and model-based RLHands-on experience with RL frameworks such as Stable-Baselines, RLlib, or PyTorch / JAX implementationsStrong Python skills and experience building end-to-end ML pipelinesComfortable working with tabular, time-series, and simulation-generated dataPreferred / Nice to Have :
Experience with agent-based modeling, digital twins, or hierarchical / multi-agent RLExperience in high-stakes, regulated, or mission-critical environmentsFamiliarity with uncertainty modeling, robustness testing, or safety-aware RLWhat We Value :
Systems-first mindset : thinking beyond models to real-world operational impactAbility to work in ambiguous problem spaces with incomplete dataStrong ownership, technical rigor, and ethical awareness in high-impact AI systemsWhy Join :
Work on mission-critical AI challenges that directly influence real-world outcomesHigh autonomy and deep technical ownershipShape next-generation decision-making and adaptive AI systemsLocation : Florida / Remote within the US only available.
If you're passionate about reinforcement learning, simulation, and building AI systems with tangible, high-stakes impact, this is an exciting opportunity to make a real difference.