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Carbon Analysis Engineer

Listing for: Neutreeno
Part Time position Listed on 2026-11-05
Job specializations:
  • Engineering
    AI Engineer (Applied/Software), Data Engineering
Salary/Wage Range or Industry Benchmark: 42000 - 60000 GBP Yearly GBP 42000.00 60000.00 YEAR

Job Description & How to Apply Below

ABOUT NEUTREENO

Neutreeno is the world's first emissions intelligence platform that revolutionises decarbonisation by modelling physical material and energy flows to map supply chain emissions. Rather than relying on point-in-time data, aggregated lifecycle assessments (LCAs) or generic emission factors, the platform uses fundamental process-based analysis to identify inefficiencies. The software creates digital twins of the production value chain, scaling gap analysis to pinpoint specific decarbonisation levers based on best available technology options for each organisation.

This approach turns emissions data into operational insight, the kind that cuts costs, sharpens decisions, and unlocks genuine competitive advantage.

We're a fast-moving team that values EQ just as much as IQ. Autonomy, high ownership, speed and no-ego collaboration are at our core. We seek people who care deeply and are eager to work on solving problems bigger than themselves.

THE ROLE

As a Carbon Analysis Engineer at Neutreeno, you'll take ownership of automating how our emissions and decarbonisation insight reaches customers. You'll sit at the intersection of emissions science, agentic workflows and data engineering, turning the outputs of our novel methodologies for measuring greenhouse gas emissions across complex industrial processes into carbon and performance insights, accurately and king across diverse sectors from jet engine production to energy infrastructure and pharmaceutical manufacturing, you'll collaborate with our data science, modelling and customer-facing teams to replace manual data validation and processing effort with production-grade pipelines, and to make sure every figure that reaches a customer is one we can stand behind.

This is a role for someone who thrives in a dynamic, fast-paced startup and enjoys automating complex workflows by building AI agents; analysing datasets to uncover and resolve irregularities; managing multiple work streams and priorities; and continuously learning at the intersection of engineering, climate science and AI.

This position operates on a hybrid work model with 3 days per week in our Cambridge office. Fully remote arrangements will be considered for outstanding candidates, provided there is sufficient overlap with UK working hours.

THE WORK YOU’LL DO
  • Own the current measurement pipeline: interpretation, extraction and validation of customer-provided supply chain and manufacturing data, as well as verification of emission modelling assumptions and outputs.
  • Deliver end-to-end automation of the customer data pipeline (ingestion -> extraction -> validation) turning a semi-manual process into a scalable, fully automated, auditable pipeline.
  • Design, build, deploy and maintain AI agents that automate data extraction, validation and analysis that use the process engineering-based tools that you will develop (mass and energy balance, energy benchmarking, etc.).
  • Team up with cross-functional partners to shape new reporting features, translating technical breakthroughs into functionality that helps customers identify, track and amplify decarbonisation wins across their entire value chains.
Requirements THE MINIMUM QUALIFICATIONS YOU NEED
  • Bachelor’s or master’s degree in chemical engineering or mechanical engineering or a related field.
  • 0-2 years professional experience in building agentic workflows. Professional experience is preferred, but a strong, relevant fourth-year degree project is more than acceptable.
  • A solid understanding of industrial processes, energy systems and mass flow analysis.
  • 1+ year of experience in Python-based tools: placements, internships and substantial projects on building pipelines and reusable tooling rather than one-off analysis scripts.
  • Familiarity…
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