Machine Learning Researcher

About Augur

Augur transforms legacy sensor systems into real-time operational intelligence. We help organisations act before threats escalate – not after damage is done. Everything we build is privacy-first by design.

Our platform connects to existing cameras and sensors to detect threats, retrace events, and surface real-time insights – all without replacing a single device. Augur helps teams reduce risk, cut costs, and grow revenue through better visibility, faster decisions, and fewer blind spots.

Our culture is built on a foundation of radical candor and mutual trust. We recognise that being an industry leader requires more than just building exceptional products – it requires empowering a team of exceptional people. Individually, we operate with high autonomy; collectively, we’re unified by a shared drive to achieve our mission through a commitment to excellence.

We are looking for an ML Researcher to join our Machine Learning team in London.

This role sits at the intersection of cutting-edge research and mission-critical deployment. You will own research directions end-to-end, developing novel algorithms at the intersection of computer vision, deep learning, metric learning, 3D geometry, and vision-language modelling.

You will formulate problems, design experiments, and drive your work from prototype through to production-grade systems that power real-time spatial intelligence across complex environments. We aim to publish at top venues and deploy at scale, and we're looking for researchers who want to do both.

The role includes opportunities to shape Augur's research roadmap, mentor junior researchers and interns, and strengthen our presence in the research community. Work on-site at least two days weekly, specifically one day being a Thursday.

Key Skills

• Academic Track Record: PhD (or Master's with equivalent research experience) in Computer Vision, Machine Learning, or a related discipline. Published work at leading journals or conferences is considered a strong plus.

• Core CV & Spatial AI: Deep expertise in areas such as metric learning, multi-view geometry, depth estimation, 3D scene understanding, rendering techniques, feature-based tracking, self-supervised learning, or learning-based mapping and localisation.

• Engineering Rigor: Strong Python and PyTorch skills; ability to write testable, production-quality code and participate actively in code reviews.

• Autonomy: Capable of independently formulating research problems, defining technical direction, and communicating findings clearly in both writing and discussion.