Reinforcement Learning Engineer Remote | $125k–$225k My client is a rapidly growing AI technology company building infrastructure for the next generation of AI systems. Backed by significant VC funding and working with startups, major enterprises and leading AI labs, they are looking for a Reinforcement Learning Engineer to build the products and internal tools powering their RL data platform. This is a highly hands-on role where you’ll own products end-to-end , working across frontend, backend, databases, data collection, vendor workflows and observability. You don’t need to be an RL researcher, but you’ll need to be comfortable working closely with research engineers and translating ambiguous requirements into polished, production-ready software. The role: Build product-facing tools for browsing environments, inspecting trajectories, reviewing task quality and debugging model behaviour Develop workflows that allow external partners to create, submit, test and iterate on RL environments and training data Create dashboards and observability tools covering environment quality, evaluation results, data collection, grader issues and pipeline health Design backend services and APIs connecting task authoring, data collection, evaluation, QA/QC and RL training infrastructure Work closely with research, engineering, operations and commercial teams to turn complex requirements into effective products Own projects from initial concept through to deployment and ongoing improvement The ideal candidate will have: Strong full-stack engineering experience, including Python and a modern web stack such as React, TypeScript or Next.js Experience owning user-facing or internal products end-to-end Experienced within Refinforcement Learning specialist organisations/projects Experience with cloud infrastructure, Docker, CI/CD, observability and production debugging High agency and the ability to identify what needs to be built without relying on a perfectly defined specification Excellent communication skills and the ability to work across engineering, research, operations and external partners Experience with data collection, annotation, evaluation, research tooling, developer tools, internal platforms or complex workflow products would be particularly valuable. Knowledge of AWS, Kubernetes, Terraform or Grafana is useful, but these are viewed as tools for shipping products rather than the focus of the role. This is an excellent opportunity for a strong full-stack engineer with experience in AI and reinforcement learning , that offers significant ownership and the opportunity to build products that directly support cutting-edge AI development.