AI/ML Engineer
(RL & Physical Systems)
FLUIX is building the AI Operating System for data centers. We deploy 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. Your work will touch real chillers, real cooling loops, and real megawatt-scale infrastructure.
Who you’ll work closely with
Abhi Sastri
Founder & CEO
Chase Overcash
CTO
What you’ll do
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.
Your background
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
We are looking for obsessed builders who want their work to matter at physical scale.
You are energized by hard problems and high-stakes environments.
You want to touch hardware, not just notebooks.
You believe AI belongs in the physical world, not just on cloud GPUs.
You thrive in “build it, ship it, iterate” environments rather than academic cycles.
Due to our mission-critical work, you are eager to help teammates and co-workers during holidays, weekends, and emergencies.
You are cordial and over-communicate with teammates, co-workers, and management.
Benefits
Competitive Salary
Attractive compensation package, including equity options.
Benefits
Comprehensive health, dental, and vision insurance, along with other standard benefits.
Work Environment
A dynamic and collaborative San Francisco Bay Area work environment.
Growth Opportunities
Opportunities for professional growth and development, with the chance to shape the future of technology in the industry.
AI/ML Engineer
(RL & Physical Systems)
Benefits
Competitive Salary
Attractive compensation package, including equity options.
Benefits
Comprehensive health, dental, and vision insurance, along with other standard benefits.
Work Environment
A dynamic and collaborative San Francisco Bay Area work environment.
Growth Opportunities
Opportunities for professional growth and development, with the chance to shape the future of technology in the industry.
www.fluix.ai
Full-time I San Francisco
Apply for this role
FLUIX is building the AI Operating System for data centers. We deploy 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. Your work will touch real chillers, real cooling loops, and real megawatt-scale infrastructure.
Who you’ll work closely with
Abhi Sastri
Founder & CEO
Chase Overcash
CTO
What you’ll do
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.
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.
Analyze telemetry, time-series, and sensor data to evaluate model reliability, interpret failure cases, and propose improvements.
Collaborate with controls, software, and field engineering teams to integrate models into production-scale data centers and energy systems.
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.
Your background
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.
Culture Fit
We are looking for obsessed individuals who want to give it their all.
We are not afraid to get our hands dirty with physical and software systems.
We are eager to visit and work with clients and understand the importance and gravitas of their mission-critical work.
We are eager to come into the office and on-site, as our work directly affects physical environments.
Due to our mission-critical work, we understand and our eager to help our teammates and co-workers during holidays, weekends, and emergencies.
We are cordial and over-communicate with teammates, co-workers, and management.