Back-End Software Engineer
Listed on 2026-02-24
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Software Development
Data Engineer, Machine Learning/ ML Engineer
The Arctic is a place of extremes: unforgiving, untamed, and undergoing rapid change. A literal defrosting reveals a multi-trillion-dollar opportunity spanning energy, defence, logistics, research, and infrastructure.
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 roleAs a Back-End Software Engineer, you’ll work across the stack as we build our Arctic OS that integrates hardware feeds, varied data sources, and machine learning algorithms. You'll iterate quickly, shipping production code that powers systems operating where connectivity is sparse and failure isn't an option. This is a role for a solid generalist — someone who sees themselves as an enthusiastic dabbler, a jack of all trades, or a tinkerer at heart — content to learn new skills and move across projects as requirements evolve.
Coreresponsibilities
- Design and implement REST APIs and web microservices
- Build and maintain data ingestion pipelines and processing systems
- Manage and develop cloud infrastructure
- Architect and optimise database systems
- Implement telemetry, logging, and monitoring solutions
- 3+ years building production backend systems
- Experience with cloud platforms
- Intellectual curiosity and rapid learning abilityyed in the real-world.
- Security engineering experience
- GIS or geospatial data handling
- Machine learning or data science background
- Time-series data and databases (e.g. Influx
DB, Timescale
DB) - Data workflow orchestration (e.g. Airflow, Dagster)
- Ruby on Rails or Python
- Kubernetes and Helm Charts
- Infrastructure as Code (Pulumi, Terraform)
- Competitive compensation.
- Real field exposure – travel to Arctic sites and Outposts when needed.
- Mission-driven culture – focus on impact, not hours logged.
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