About this opportunity

This role is being recruited by Calyptus on behalf of one of our clients.

Calyptus is a specialist recruitment partner helping startups and high-growth technology companies hire across engineering, product and design.

What to expect

• We’ll review your application and, if your experience is a strong match, one of our team will be in touch.
• We’ll support you throughout the interview process and keep you updated at each stage.
• If this particular role isn’t the right fit, we may consider you for other relevant opportunities with our clients.

What you will do

The decision-making algorithm

• Design and build the core algorithm that turns live sensor data into decisions: which setpoints to change, by how much and when.
• Frame the problem properly. Decide whether it is best treated as prediction plus optimisation, model predictive control, Bayesian optimisation, contextual bandits, offline reinforcement learning or a combination, and justify the choice with evidence.
• Build process models that link settings and operating conditions to quality, scrap, throughput and energy, and that respect the dynamics of the process, including time delays, dead time and variation in raw material.
• Learn from historical data in which settings were rarely varied deliberately. Deal with confounding, operator interventions and changes of regime, and design safe experiments on live lines to fill the gaps.
• Quantify uncertainty and build in the ability to make no recommendation when conditions fall outside what the model has seen.
• Evaluate decisions properly: offline policy evaluation before anything goes live, and controlled trials against real production outcomes afterwards.

Sensor data and features

• Build the pipeline from raw sensor data to model-ready features, covering synchronisation, resampling, lag alignment, filtering, missing data and faulty sensors.
• Develop soft sensors for quality measures that are only taken occasionally or offline.
• Link process data to production context such as product, recipe, material batch and shift.
• Detect anomalies, sensor faults and model drift, and retrain as lines, materials and products change.

From recommendation to setpoint change

• Work out, for each adjustable variable, when a recommendation can safely be applied, taking account of process state, operating envelope, rate of change, interaction with other variables and the time the process needs to settle.
• Design the supervisory layer that sits above the line's existing PLC control loops, adjusting their setpoints while those loops continue to regulate the process, and make sure it never works against them.
• Define the conditions under which the system must hold or hand control back to the operator, such as start-up, product changeovers, alarms, sensor faults or unfamiliar material, and make handover between manual and automatic operation smooth.
• Monitor the effect of every change and reverse it automatically if the process does not respond as expected.
• Design the staged route from advisory to closed-loop operation, with clear, measurable criteria for when a line, or an individual variable, is ready for the next stage.
• Specify how setpoints are written back to the PLC (range checks, rate limits, reversibility and fallback to the last known good state), working with our integration partner and the customer's engineers. Machine safety functions stay in the PLC's safety layer and are never overridden by our software.
• Present recommendations with the reasoning behind them, in a form operators can check quickly and trust.