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Gigaton

Self-learning process-control software for energy-intensive plants

Solutions Engineer

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-timeLondon, UK

About the Job

Requirements

  • A strong process or control engineering background, ideally in cement, chemicals, energy, or heavy manufacturing.
  • Ability to collaborate with operators, plant managers, engineers, and data scientists, connecting industrial knowledge with data-driven insights.
  • Analytical ability to examine industrial data, identify root causes, develop actionable solutions, and deliver outcomes.
  • Ability to communicate effectively and build credibility and trust in complex industrial environments.
  • Project management skills, including working independently, driving actions for oneself, the team, and the customer, delivering against plans, and managing project risks.
  • Willingness to travel regularly to deployment sites, approximately 25%.
  • Experience with industrial control systems (APC/MPC/PID), automation systems (DCS/PLC), or data systems (OPC, historian, LIMS).
  • Understanding of AI/ML applications such as forecasting, optimisation, and anomaly detection.
  • Experience implementing or tuning control systems and understanding how to balance process stability, quality, and energy efficiency.
  • Ability to work across technical, operational, and business interfaces, structure solutions to complex problems, and communicate complex ideas to multiple audiences.

Responsibilities

  • Build trust and engagement with plant managers, process engineers, and operators by explaining technical decisions and demonstrating measurable benefits aligned with operational needs.
  • Analyse plant data to assess and identify opportunities and evaluate the performance of Gigaton’s software.
  • Develop and refine control strategies using APC, MPC, and PID principles.
  • Deploy and integrate Gigaton AI into live plants, ensuring stable, safe, and effective operation.
  • Collaborate with plant and process teams to identify optimisation opportunities, tune control loops, and validate performance improvements.
  • Collaborate with machine learning engineers to refine algorithms and recommendations based on real-world outcomes.
  • Create and refine deployment playbooks and templates to support repeatable, scalable, high-quality rollouts across plants.
  • Provide field insights to Gigaton’s product and machine learning teams to inform the product roadmap.
  • Travel to sites approximately one week per month to work hands-on with teams during commissioning, testing, and optimisation phases.

Desired Qualifications

  • Thriving in dynamic, high-impact environments, with motivation to work in fast-paced, ambiguous settings where deployments contribute to decarbonisation.
  • Taking ownership of projects and seeing them through end-to-end, identifying problems and acting decisively to solve them.

About the Company

Gigaton

Self-learning process-control software for energy-intensive plants

Gigaton builds self-learning process-control software for energy-intensive plants in cement, steel, glass and chemicals. The system sits inside existing plant infrastructure and uses simulations and predictive models to test decisions before applying them to real equipment like fuel mix, kiln speed and oxygen levels. It also explains actions to operators and retrains on live plant data to improve over time. Unlike many AI tools, Gigaton’s platform continuously learns from live plant operation and provides transparent reasoning for its recommendations. It targets large sectors that contribute a big share of global greenhouse-gas emissions and is backed by academic origins, established investors, and a dedicated industrial-scale focus. The company aims to scale autonomous industrial control to cut emissions from major energy-intensive industries by optimizing operating decisions and keeping humans in the loop through explanations.

Company size
11-50
Company stage
Series A
Total funding
$47.1M
Headquarters
London, United Kingdom
Founded
2020

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