ML Engineer Research Scientist - Cryosphere - Arctic Research and Development

ML Engineer (Scientific Research - Cryosphere)

FULL-TIME | UK & ARCTIC | DEV

LOCATION: CALMSDEN, CIRENCESTER, UK

THE LAST FRONTIER

ARD was founded on a paradox. The Arctic is both the next great economic frontier and one of the most hostile environments on Earth. Rich in resources and strategically vital, yet bone-chillingly cold, dark for months, and fundamentally inhospitable to humans. This paradox—an explosion of activity in a place that resists human presence—is why we exist.

Our solution is autonomy.

We build systems that operate reliably and intelligently in the Arctic, so that humans don't always have to.

Founded by record-holding pioneers with decades of polar experience, we take calculated risks on hard problems that matter. If you want to build and deploy ground-up technology with real-world impact, at an inflection point in history that won’t reappear, we’d like to talk.

THE ROLE ARD is building Auka - an intelligence layer for the Arctic that turns a flood of observations into understanding, foresight, and action. Our approach is deliberately grounded in modern science. We develop and run physics models, collect empirical data in the field and laboratory and then use that knowledge to extract meaning from our diverse datasets. We avoid black boxes — the products we ship trace back to the physics and data that produced it.

This role focuses on developing and tuning machine learning pipelines that are fast, trustworthy, and traceable, ingesting diverse datasets with an emphasis on remotely sensed imagery. These pipelines will form the foundational products on which our longer-term Arctic intelligence platform will build.

This is the first science hire and a senior one. You will own the Analyse-and-ML workstream, working directly with the Science Lead, set the technical direction for how physics and ML meet at ARD, and become a pivotal founding member of the ARD science team.

CORE RESPONSIBILITIES

Distil reference physics into fast operators - build emulators / surrogates that run orders of magnitude faster than our reference physics models.

Own the forward-to-inverse bridge - Build the ML that inverts real hyperspectral and multispectral imagery into physical quantities (albedo, melt state, impurity loading), with the physics as the constraint, not an afterthought.

Make trust a property of the model, not a report - bake in uncertainty quantification, provenance, and validation against cold-lab and field measurements. Outputs must be defensible pixel-to-decision.

Set the technical bar for physics ML across the science team, and help shape and mentor the hires that follow you.

Ship into the platform - work with the Science Lead and Head of Software so your models land in production — not just in papers.

REQUIREMENTS