Founding ML Engineer - Industrial AI for Smart Manufacturing

Calyptus; Greater London; In loco; GBP 90.000 - 130.000; Tempo pieno

Descrizione del lavoro

Calyptus, on behalf of a client in Greater London, is seeking a senior ML/DS expert to design algorithms that convert live sensor data into actionable setpoints for industrial processes. You will frame problems, build models, and deploy robust, production-ready solutions.

You will work with time-series data, uncertainty quantification, and MLOps practices, interfacing with plant engineers and operators to ensure safe, effective control across continuous processes.

Competenze

• Minimum 6 years in applied ML/data science with sensor/time-series data from physical systems.
• At least one production model influencing operational decisions.
• Strong time-series modelling and probabilistic methods.
• Experience with optimization or sequential decision-making.
• Good grasp of causal reasoning.
• Proficiency in Python and ML stack with CI, Docker, Linux.

Mansioni

• Design and build the core algorithm turning live sensor data into decisions.
• Justify modeling approach with evidence across production contexts.
• Develop models linking settings to quality, scrap, throughput and energy.
• Quantify uncertainty and design safe experiments on live lines.
• Evaluate offline policy evaluation and controlled trials.

Conoscenze

Time-series modelling

Probabilistic methods

Optimization

Model predictive control

Reinforcement learning

Python

PyTorch

Docker

Linux

Causal reasoning

Formazione

PhD or MSc in ML/Stats/Engineering

Equivalent practical experience

Strumenti

MLflow

ONNX Runtime

Time-series databases

Cloud platform

PLC interfaces

Descrizione del lavoro

Calyptus, on behalf of a client in Greater London, is seeking a senior ML/DS expert to design algorithms that convert live sensor data into actionable setpoints for industrial processes. You will frame problems, build models, and deploy robust, production-ready solutions.

You will work with time-series data, uncertainty quantification, and MLOps practices, interfacing with plant engineers and operators to ensure safe, effective control across continuous processes.