The role asks for a hands-on machine learning engineer who can build, test and ship models, optimisers and controllers that run real industrial plants. A strong candidate has a couple of years of applied ML work, solid Python and PyTorch or scikit-learn skills, and has taken a model all the way into production. A science or engineering background helps, since the models have to respect how physical processes behave.
How it fits the AI era
AI is the core of this work, as learning systems take over fine tuning of plant settings that people once adjusted by hand. Code assistants can speed up routine engineering, but deciding which method fits a furnace or kiln, and checking that it is safe and useful, still needs human judgment. Working closely with plant operators and domain experts also stays a human job.
How it builds AI experience
Someone in this role would gain experience with control focused AI, such as active learning, time series models and optimisation running live on heavy industry sites. To build toward it, try small projects in reinforcement learning or system identification on public process or simulation data, and practise deploying models end to end in the cloud.
Worth showing
- Python and PyTorch projects
- End to end ML deployment
- Time series or control modelling
- Physics or engineering background
- Cloud infrastructure experience
This is our own read, written from the employer’s posting and the company’s Spotlight. The employer’s posting is the source of truth for the role.
About Gigaton
Industrial artificial intelligence platform providing self learning process control to cut fuel costs and carbon emissions in energy intensive manufacturing.
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