Senior Reinforcement Learning Engineer

Job Description

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 learning
• Strong foundation in MDPs, POMDPs, policy gradients, value-based methods, and model-based RL
• Hands-on experience with RL frameworks such as Stable-Baselines, RLlib, or PyTorch/JAX implementations
• Strong Python skills and experience building end-to-end ML pipelines
• Comfortable working with tabular, time-series, and simulation-generated data

Preferred / Nice to Have:

• Experience with agent-based modeling, digital twins, or hierarchical/multi-agent RL
• Experience in high-stakes, regulated, or mission-critical environments
• Familiarity with uncertainty modeling, robustness testing, or safety-aware RL

What We Value:

• Systems-first mindset: thinking beyond models to real-world operational impact
• Ability to work in ambiguous problem spaces with incomplete data
• Strong ownership, technical rigor, and ethical awareness in high-impact AI systems

Why Join:

• Work on mission-critical AI challenges that directly influence real-world outcomes
• High autonomy and deep technical ownership
• Shape next-generation decision-making and adaptive AI systems

Location: 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.