TL;DR

Principal Quant Engineer (AI): Designing and building production-grade quantitative models for environmental and air quality metrics with an accent on integrating mathematical modeling into scalable software systems. Focus on applying machine learning techniques, ensuring model reproducibility, and collaborating with cross-functional teams to deploy analytical solutions into live production environments.

Location: Must be based in or able to work in London, UK (Hybrid: 8 days/month in office)

Company

%hirify_global% is a global leader in professional information, software solutions, and services for clinicians, accountants, lawyers, and tax, finance, audit, risk, compliance, and regulatory sectors.

What you will do

Design, implement, and validate quantitative mathematical models for air quality, emissions, and environmental metrics.
Develop production-ready, scalable, and maintainable model implementations that integrate into backend services and data pipelines.
Apply AI and machine learning techniques such as regression, time-series, and probabilistic models to real-world datasets.
Perform uncertainty quantification and model validation, ensuring transparency for technical and non-technical stakeholders.
Collaborate with platform and product engineering teams to deploy models into live production systems.

Requirements

Strong quantitative background in mathematics, statistics, physics, engineering, or economics.
Must have extensive experience building financial, environmental, or air quality quantitative models.
Strong proficiency in Python or C# is required.
Practical experience with ML libraries like PyTorch, TensorFlow, or scikit-learn.
Must be able to work on-site in London for at least 8 days per month.
Strong ability to document and explain complex model assumptions and methodologies.

Nice to have

Experience with time-series analysis and geospatial data.
Knowledge of MLOps and model lifecycle management.
Experience working within SaaS or product-led organizations.
Familiarity with environmental or financial regulatory reporting standards.

Culture & Benefits

Opportunity to build high-impact models that influence global environmental decision-making.
Dynamic, global technology environment with a focus on professional development.
Collaborative work culture bridging the gap between scientific theory and software engineering.
Hybrid work model providing flexibility while maintaining team connection.

Hiring process

Evaluation of individual skills and communication style without the use of AI tools or external prompts.
Candidates may be required to attend in-person interviews at the office.