ML Engineer (Research Scientist - Cryosphere)

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.

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

We are looking for a scientist who lives on the seam between physical modelling and machine learning — someone whose instinct is to respect and accelerate physics, not replace it with an opaque network.

• Essential
• A PhD (or equivalent research track record) in a physical, geophysical, computational, or ML discipline, with a body of work at the physics–ML interface: physics-informed / hybrid models, emulators or surrogate models, neural operators, differentiable simulation, or data assimilation with learned components.
• Fluency with uncertainty quantification and with validating models against real measurements.
• GIS or geospatial data handling.
• Machine learning or data science background.
• Strong scientific software engineering: production-quality Python, reproducibility, version control, testing. Your models are meant to ship.
• Seniority to own a workstream and be delegated to — able to set direction, make sound calls under ambiguity independently, and represent the science externally.
• Comfortable being the first, and helping build the team.
• Strongly preferred
• Working knowledge of the cryosphere (glaciology, ice-surface / snow, or sea-ice physics).
• Experience with Earth-observation / remote-sensing retrieval — inverting satellite or drone imagery into physical quantities, atmospheric correction, cal/val.
• The cloud-native geospatial stack (Xarray, Zarr, Dask, STAC; the Pangeo ecosystem).

WHAT WE VALUE

• You reach for the simplest model that respects the physics before the biggest one.
• You treat provenance and uncertainty as first-class, not documentation added at the end.
• You've shipped something real that others depend on — an open-source tool, an operational model, a product.

WHAT WE OFFER

• Competitive compensation.
• Private Health Insurance.
• Dental & Optician.
• Equity — meaningful options as an early employee at an early-stage company.
• Real field exposure — travel to Arctic sites and Outposts when needed.
• Mission-driven culture — focus on impact, not hours logged.
• Small team, real ownership — what you build matters and ships.