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AI Engineer — Industrial Process Optimisation
Erik Hoffmann
Full-time · Mid-level · Chicago
About the role
We make cement. We have been making cement since 1961. Three of our four plants are in the US Midwest; the fourth is in northern Ontario. We employ 1,400 people. Our margins are thin, our energy costs are significant, and the gap between an optimised kiln and an unoptimised one is meaningful in both operational cost and emissions per tonne. We are not a tech company. We do not have a ping pong table or a cold brew on tap. We have engineers who have worked on the same plant for twenty years and know things about kiln behaviour that aren't written anywhere. We are hiring our first dedicated AI engineer to work alongside those people. The job is to build predictive models and anomaly detection systems for kiln temperature profiles, fuel consumption, and quality parameters — things that our plant engineers can actually use and that improve when our plant engineers give feedback on why the model got something wrong. The role requires someone who can earn the trust of a process engineer who has never used a machine learning model, because that is the only way the models will get used. If you've worked in manufacturing, energy, or process industries, or if you're simply excited by the challenge of making ML useful in an environment that didn't grow up around it, we'd like to hear from you.
Responsibilities
- Build and deploy predictive models for kiln temperature optimisation and fuel efficiency at two initial pilot plants
- Develop anomaly detection systems for quality parameter drift using sensor time series data
- Work with plant engineers to understand the physical process context behind model inputs and outputs
- Build simple, interpretable monitoring dashboards that process engineers can use without technical support
- Document every model's inputs, assumptions, and known limitations in language a plant manager can read
Requirements
- 3–5 years of ML or data science engineering with production deployment experience
- PyTorch or equivalent for time series modelling — anomaly detection, regression, and predictive maintenance patterns
- Python for the full stack: data ingestion from plant historians, feature engineering, model training, and deployment
- Computer Vision experience for camera-based quality inspection — not required but used in our secondary inspection line
- Docker for consistent deployment environments across our four plant data centres
- Strong verbal communication — you will regularly explain model outputs to people with no ML background
Benefits
- Work on a real industrial problem where a 2% efficiency improvement has a measurable financial and emissions impact
- Hybrid — primarily remote with monthly on-site visits to our pilot plant in Joliet, Illinois (travel covered)
- $92,000 – $112,000 base salary
- $1,000 annual training budget
- Stable company with no venture capital, no runway pressure, and a plan to operate for another sixty years
Job Type
Full-time
Level
Mid-level
Language
English
Salary Range
$92,000 – $112,000
AI Expertise
AI & Machine Learning Engineers
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