AI/ML Engineer

Overview FLUIX is building the AI Operating System for data centers, deploying autonomous AI that optimizes, predicts, and controls AI factories. Based in the San Francisco Bay Area, we develop intelligent control systems that enable data centers and power providers to operate faster, cleaner, and more efficiently. Our mission is simple: help clients double their compute capacity without wasting resources. We’re hiring an AI/ML Engineer (or AI Scientist, depending on experience) with deep reinforcement learning and physics‑based modeling expertise. You’ll design, test, and deploy models that interact with the physical world—from thermal systems to power distribution—where milliseconds and megawatts matter. This is not a research‑only position: you’ll work on real chillers, real cooling loops, and megawatt‑scale infrastructure. Responsibilities Design, develop, and deploy reinforcement‑learning based control policies for real‑world physical systems (cooling, power, airflow, thermodynamics, etc.). Build and refine digital twin and simulation environments to accelerate training, testing, and Sim2Real deployment. Conduct lab‑based and field‑based experiments to validate model performance under noisy, dynamic, and safety‑critical conditions. Analyze telemetry, time‑series, and sensor data to evaluate model reliability, interpret failure cases, and propose improvements. Support integration of LLM‑based tools and workflows into the AI control pipeline where relevant (knowledge distillation, inference orchestration, etc.). Lead or contribute to scientific documentation: whitepapers, internal reports, and peer‑reviewed publications. Push the frontier of physical‑world AI, where physics, reinforcement learning, and industrial automation meet. Collaborate with controls, software, and field engineering teams to integrate models into production‑scale data centers and energy systems. Qualifications Bachelor’s degree required in Computer Science, Mechanical/Electrical Engineering, Applied Physics, Controls, or related field. Master’s or Ph.D. strongly preferred for the AI Scientist tier. 2+ years of hands‑on experience applying ML to real‑world physical, robotic, industrial, or control systems. Proficiency in Python and ML frameworks (PyTorch, TensorFlow); experience with RL libraries. Strong grounding in at least one of: control theory, model‑predictive control (MPC), system identification, thermal/fluids, power systems, or industrial automation. Experience working with telemetry/sensor data from PLCs, SCADA, IoT, or industrial control systems. Familiarity with cloud or edge deployment (AWS/Azure, on‑prem GPUs, embedded compute). Ability to move between research, experimentation, and deployment at startup speed. Bonus Points Experience deploying AI in data centers, utilities, industrial automation, HVAC, or energy systems. Experience with digital twins, physics engines (Modelica, Simulink, custom simulators). Publications, patents, or open‑source work in RL, controls, or applied physical AI. Experience with Sim2Real transfer, safety‑critical RL, or physics‑informed ML. Experience with LLMs, agentic AI workflows, or hybrid RL + LLM systems. Culture Fit Obsessive builders who want their work to matter at physical scale. Energized by hard problems and high‑stakes environments. Want to touch hardware, not just notebooks. Believe AI belongs in the physical world, not just on cloud GPUs. Thrives in “build it, ship it, iterate” environments. Ready to help teammates during holidays, weekends, and emergencies. Communicative and over‑communicate with teammates, co‑workers, and management. Benefits Competitive salary and equity options. Comprehensive health, dental, and vision insurance. Dynamic and collaborative San Francisco Bay Area work environment. Opportunities for professional growth and development. #J-18808-Ljbffr Nashville Public Radio