Materials & Process Scientist: Model, Validate & Optimize

Atmospheric AI; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Atmospheric AI Limited in London is seeking a strong modelling specialist to build physics-based transformation and conversion models for materials economics and carbon accounting. You will enforce mass and energy balances and validate models against plant evidence, licensor data and literature.

You will work on canonical material tables and interfaces with Python-based tooling, Databricks or MLflow, and collaborate with teams to ensure robust, production-ready models.

Qualifications

• Strong chemical and process-engineering fundamentals required.
• Experience with mass and energy balances and thermodynamics.
• Ability to design validation tests and set tolerances.

Responsibilities

• Build transformation models with energy and mass balance constraints.
• Develop conversion models for materials processing (extrusion, molding, etc.).
• Create a durable canonical materials table and ensure data integrity.
• Implement automated validation tests against model outputs.
• Work across metals, paper, fibres or new chemical systems.
• Contribute to a robust data-modelling framework using Python.

Skills

Mass & energy balances

Thermodynamics

Reaction engineering

Process economics

Unit operations

Materials knowledge

Validation mindset

Python modelling

Modular code

Canonical tables

Education

Master's or PhD in chemical, process or materials engineering or polymer science

Tools

Python

PostgreSQL

Dagster

AWS

Databricks

MLflow

Job description

Atmospheric AI Limited in London is seeking a strong modelling specialist to build physics-based transformation and conversion models for materials economics and carbon accounting. You will enforce mass and energy balances and validate models against plant evidence, licensor data and literature.

You will work on canonical material tables and interfaces with Python-based tooling, Databricks or MLflow, and collaborate with teams to ensure robust, production-ready models.