Core Responsibilities

• Distill 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

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). We will weight the ability to reason fluently about physical models over any specific sub-domain, and we also highly value marine and atmospheric science—but you should be able to engage with radiative-transfer and ice-surface physics without hand-holding.
• 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.

Why ARD

• The mission - The Arctic is the fastest-changing region on Earth. You'll build the intelligence layer that helps understand and steward it—an early-warning system for the planet's coldest edge.
• Science and product - Our model rewards work that yields both new science and a platform others build on. You won't have to choose between publishing and shipping.
• Foundational scope - You're the first science hire, you'll work directly with the Science Lead and Head of Software. You'll be instrumental in shaping the team that follows.
• Open by design - A free Arctic data commons alongside paid services—your work reaches the whole field, not a walled garden.

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 (genuinely, not as a gimmick).
• Mission-driven culture—focus on impact, not hours logged.
• Small team, real ownership—what you build matters and ships.