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# AI/ML Engineer

- **Company:** FLUIX INCORPORATED
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Data Centers, Supervisory Control and Data Acquisition (SCADA), Python (Programming Language), Open Source Technology, Tensorflow, Simulink, Reinforcement Learning, Digital Twin, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Agentic-AI, Information Technology, Process Control Systems, Modelica
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_d0b9383a2f2b6a70e7cc811f1a170a26

## About the Role

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., 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.

## Description

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.