AI Research Scientist

About Phaidra Phaidra is building the future of industrial automation. We create AI-powered control systems for the industrial sector, enabling facilities to automatically learn and improve over time using reinforcement learning. We focus on industrial applications with measurable KPIs and allow domain experts to configure AI agents without writing code. Our team has a track record of applying AI to challenging problems and delivering real impact. We value Agency, Velocity, Craft and Truth, and we operate as a 100% remote company with employees located in the USA and globally. Who You Are Research Scientists lead efforts in developing novel algorithmic architecture to bring intelligent control systems to the industrial sector. You will work across disciplines including model-based reinforcement learning, planning and control, deep learning, world models, and safe reinforcement learning to advance production-ready AI systems. Responsibilities Design, implement, and evaluate model-based reinforcement learning agents (including MPC/MPPI-based controllers) and prototypes for deployment on real industrial systems. Develop learned dynamics and world models that generalize across systems, including training pipelines and fine-tuning for reliability in planning and control. Explore safe/constrained RL, scenario planning and Bayesian RL to meet safety constraints; report findings clearly; collaborate with external partners; mentor Research Engineers; define new research directions; translate research into practical outcomes; own development and rollout for a research area. Key Qualifications PhD in a technical field with strong background in model-based reinforcement learning and knowledge in planning, world models, RL, deep learning, control theory, safe RL. 2+ years post-PhD or 5+ years post-Master’s research experience; extensive research in ModelBased/ModelFree/Safe RL and control theory; hands-on experience with simulators and sim-to-real; alignment with Phaidra values. Preferred Skills & Experience PhD in ML, control or related field; deep, practical experience with model-based RL and planning in real-world systems; strong Python and PyTorch skills; publications in RL, control; passion for AI and industrial applications. Our Stack Python, PyTorch, scipy, Kubernetes, Docker, Ray, GCP Onboarding A multi-stage onboarding program detailing our product, research tracks, codebase, and environment setup.