Hiring nowChemistry, materials and process discovery

AI Process Optimisation Engineer

Also called Chemical Process AI Engineer

An AI Process Optimisation Engineer builds models of chemical or industrial processes and uses them to raise yield and efficiency without breaching safety limits. They turn plant data and scientific knowledge into changes a site can actually trial.

What they do

They start with a process: a reactor, a separation step, a coating line. They gather the data, find out what is measured and what is not, and build a model that respects mass balance, kinetics and equipment limits. Then they search for better operating settings using constrained optimisation, often alongside learned surrogate models. They plan experiments to fill gaps, work with process engineers and operators on what is safe to try, and review results after each trial. They also document assumptions so others can challenge them.

What a strong hire delivers

In the first months, a strong hire delivers a working process model that process engineers trust and can question. They agree the constraints and safety bounds with the people who own the plant. They produce a first optimisation proposal, tested in simulation and then in a controlled trial, with a clear account of what improved, what did not and why. They leave behind data and method others can reuse.

How you can tell they are good

Ask them to walk through one process they optimised end to end. Look for specifics: which constraints mattered, how they handled noisy or missing data, and how they decided a change was safe to test. Strong people explain where their model was wrong. Be wary of anyone who talks only about model accuracy and never about limits, operators or failed trials.

Why it is hard to fill

The title varies widely, so searching by it misses many good people. The work sits between chemical engineering, scientific machine learning and operations research, and few people are strong in all three. Machine learning engineers often lack process safety instinct. Process engineers often lack modelling depth. Candidates who have shipped a change to a real plant are scarce, and they are often content where they are.

Where to find them

Look at chemical and process engineers who moved into modelling, optimisation or data roles. Also look at computational chemists and materials scientists who have worked with plant or pilot data, and operations research people from energy, manufacturing and specialty chemicals. Good candidates come from process manufacturers, materials start-ups, industrial software teams and research groups working on scientific machine learning.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Process Optimisation Engineer

  1. Tell us about a process you optimised. What were the constraints and what changed in practice?

    A strong answer names real constraints, the baseline, the change made and the measured result. It covers who signed off the trial. Worry if the story is only about a model score, or if they cannot say what happened on the plant.

  2. How do you decide what is safe to test when your model suggests settings outside past operating data?

    They should describe bounded exploration, uncertainty estimates, engineering review, staged trials and abort conditions. They should respect process safety owners. Worry if they trust extrapolation by default or treat safety limits as just another soft penalty.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for an engineer to improve how our chemical or industrial processes run. You will build models of real processes, search for better operating conditions within strict safety limits and work with process engineers to test changes. Your work will be judged by measured gains in yield and efficiency.

What you will do

  • Build and validate models of chemical or industrial processes from plant, pilot and lab data
  • Develop constrained optimisation methods that respect equipment and safety limits
  • Design experiments that close the biggest gaps in process knowledge
  • Prepare optimisation proposals with clear assumptions, risks and expected gains
  • Run controlled trials with process engineers and operators and review the results
  • Document methods and data so others can reproduce and challenge them

What you will bring

  • Demonstrated work improving a real chemical or industrial process using models
  • Practical experience with constrained optimisation or operations research methods
  • Comfort with scientific computing and building models from imperfect data
  • Clear understanding of process safety and how limits are set and respected
  • Experience designing experiments or trials and interpreting the results
  • Ability to work with engineers and operators and explain reasoning plainly

Nice to have

  • Experience with deep learning for scientific or physical systems
  • Background in molecular simulation or materials databases
  • Experience moving a model from pilot scale to production

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Scientific domain expertiseAdvancedApply disciplinary theory and experimental standards
  • Deep learningAdvancedBuild and diagnose trainable neural models
  • Experimental designWorkingChoose comparisons that identify credible effects
  • Optimisation and operations researchWorkingChoose actions subject to objectives and constraints

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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