Solutions Engineer- Control Systems

Our Mission

At Carbon Re, we’re on a mission to cut gigatonnes of carbon emissions from the world’s biggest emitting industries — like cement, steel, and glass — by applying cutting‑edge AI where it matters most.

We’re a small and growing team of scientists, engineers, and strategic thinkers who care deeply about impact and believe in getting there with good humour and urgency. Our SaaS products help heavy industry optimise operations in real time, cutting costs and carbon today while building the foundation for the next industrial revolution.

We’re seeking a Solutions Engineer – Process Control to join our Product and Solutions Engineering team and sit on the frontline of industry decarbonisation.

This role blends industrial process expertise, control engineering, and deployment leadership
. You’ll work directly with process engineers and plant operators to understand complex chemical processes, design the best AI control strategies, and integrate Carbon Re’s AI into live production environments, ensuring that our technology aligns with operational realities and delivers measurable value.

Think of it as the process engineer’s trusted partner — technical, pragmatic, and collaborative. You’ll lead deployments, translate AI outputs into actionable control strategies, and help shape Carbon Re’s approach to scaling advanced process optimisation across global industrial sites.

• Deploy & integrate Carbon Re AI into live cement and industrial plants, ensuring stable, safe, and effective operation within existing process control frameworks.

• Collaborate with plant and process teams to identify optimisation opportunities, tune control loops, and validate performance improvements.

• Analyse plant data to assess and identify opportunities, and evaluate the performance of Carbon Re’s software.

• Develop and refine control strategies
  , leveraging APC, MPC, and PID principles to bridge AI recommendations with practical plant control systems.

• Build trust and engagement with process engineers and operators by clearly explaining technical decisions and demonstrating measurable benefits.

• Collaborate with machine learning engineers to refine algorithms and recommendations based on real‑world outcomes.

• Support deployment playbooks and templates
  , ensuring repeatable, scalable, and high‑quality rollouts across plants.

• Act as a feedback link to Carbon Re’s product and machine learning teams, providing field insights that shape our product roadmap and model development.

• Travel to sites (~1 week/month) to work hands‑on with teams during commissioning, testing, and optimisation phases.

• Strong background in process or control engineering
  , ideally in cement, chemicals, energy, or heavy manufacturing.

• Experience with
  advanced process control (APC/MPC/PID) systems and process optimisation projects.

• Familiarity with industrial automation systems (e.g., DCS, PLC, OPC, historian systems).

• Understanding of AI/ML applications in process control (forecasting, optimisation, anomaly detection) is a plus.

• Comfortable collaborating with operators, engineers, and data scientists — bridging industrial know‑how with data‑driven insights.

• Analytical and hands‑on mindset: capable of diagnosing process instabilities and proposing actionable solutions.

• Excellent communication skills — able to build credibility and trust in complex industrial environments.

• Willingness to travel regularly to deployment sites (~25%).

• You’re a practical problem‑solver who enjoys working on real‑world process challenges.

• You’ve implemented or tuned control systems and understand how to balance process stability, quality, and energy efficiency.

• You’re confident working across technical, operational, and business interfaces
  , turning complex problems into structured solutions.

• You thrive in dynamic, high‑impact environments
  , where every deployment directly contributes to decarbonisation.

• You can translate between AI outputs and process logic
  , ensuring plant teams understand and trust automation strategies.

• Plants achieve measurable performance improvements —
  lower costs, reduced CO₂,…