What you'll do
- Build autonomous optimization systems for the real world — connecting AI, control, and process intelligence.
- Prototype and tune reinforcement learning, model-predictive control, or hybrid physics-ML algorithms.
About you
- You’re an ML/Control engineer who wants your models to touch the real world.
- You enjoy writing code that both trains and controls.
- You’re excited by the challenge of turning industrial data into autonomy.
What we're looking for
- Familiar with chemical process control, with a chemical engineering background, and has designed interesting DCS programs and controllers.
- Strong foundation in ML / optimization — e.g., reinforcement learning, MPC, system identification, or Bayesian methods.
- Understanding of control theory, process systems, or simulation environments is a plus.
- Experience with real-time data or streaming frameworks (MQTT, OPC-UA, Kafka, etc.) is a plus.
In order for us to better evaluate your application, kindly attach your projects (website/github repo/pdf etc) with resume.