
Sigma Nova builds large-scale AI foundations to accelerate scientific breakthroughs. It develops foundation models and Gen AI capabilities through partnerships that unlock diverse datasets from…

Sigma Nova builds large-scale AI foundations to accelerate scientific breakthroughs. It develops foundation models and Gen AI capabilities through partnerships that unlock diverse datasets from…
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The Challenge: Complex Scientific Data & Representation Learning
We’re building foundation models for scientific signals, starting with the brain (EEG, fMRI, ultrasound) and aim to extend the same approaches to other complex domains where data is scarce and difficult to collect.
We finished as silver medalists at the NeurIPS 2025 EEG challenge and have had recent papers accepted to CVPR and submitted to ICML. This feels like we’re just beginning to find our footing. For every problem we solve, we seem to stumble upon three more fundamental questions we can’t answer yet. We’re still trying to figure out the basics of how these signals behave, and we find that uncertainty is much more motivating than the ‘solved’ parts.
This role is about architecture-level thinking:
While technical excellence is critical, we place equal importance on how we work together. We believe the best teams are built on:
Understanding Failures
Pre-Training Strategy
Multimodal Alignment
End-to-End Ownership
Education: PhD (preferred) in Machine Learning, Deep Learning, or a closely related quantitative field.
Technical Expertise: Strong proficiency in PyTorch and experience running large models on GPU infrastructure
Data Experience: Hands-on experience handling challenging/complex data, including irregular time series, scientific signals, and geometric or physical structures.
The “Data-First” Mindset: A practical approach to research where you prioritise analysing raw signals and data quality over just monitoring loss curves.
Navigating Ambiguity: Ability to thrive in an environment with open-ended problems and the communication skills to make complex research approachable for non-scientific teams.
Fluent English (the team speaks English in the day-to-day)
Integrity & Respect
Open Communication & Humility
Psychological Safety & Camaraderie
Introductory call with Paul (Head of Talent Acquisition) – 30 min
Deep dive on AI research – 45 min
Behavioural interview with Paul – 30 min
Half-day onsite : Research talk + pair coding + pair system design session.