100% Remote 3+ Month Reinforcement Learning Engineer (Pricing Intelligence) Contract

Overview: Our client is building next-generation pricing intelligence capabilities that leverage reinforcement learning (RL) to optimize decisions in dynamic, real-world environments. This role is ideal for someone who thrives at the intersection of applied machine learning, experimentation, and scalable deployment — not just theory. What You’ll Do: • Design, train, and evaluate reinforcement learning agents to solve complex optimization problems in pricing and decision automation. • Develop end-to-end RL pipelines — from environment design and reward shaping to policy evaluation and tuning. • Implement and iterate on algorithms such as PPO, DQN, and contextual bandits using frameworks like PyTorch, TensorFlow Agents, RLlib, or Stable Baselines. • Deploy and monitor models in production via AWS SageMaker, building automated training, testing, and CI/CD workflows. • Collaborate cross-functionally with data scientists, engineers, and business stakeholders to bring research concepts into measurable business impact. What We’re Looking For: • Proven hands-on experience applying reinforcement learning in real or simulated environments — beyond coursework or research papers. • Strong understanding of RL principles: reward functions, exploration vs. exploitation, policy optimization, and convergence behavior. • Proficiency in Python and at least one major ML framework (PyTorch, TensorFlow, JAX, etc.). • Experience with AWS SageMaker or similar platforms for model training, deployment, and experimentation. • Familiarity with experimentation tracking tools (e.g., MLflow, Weights & Biases) and MLOps best practices. • Bonus: experience with optimization, pricing models, or applied decision systems.