Sigma Nova

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…

Artificial Intelligencesigmanova.ai

Sigma Nova

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…

Artificial Intelligencesigmanova.ai

HQParis, FR

Team Size8

Open Jobs3

Total Funding-

Latest FundraiseUnknown

Join the Team

Research Scientist — Frontier Physical AI

HybridRue de Mogador, 75009 Paris, France, FR

Hybrid • Rue de Mogador, 75009 Paris, France, FR

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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.

What the Work Looks Like

This role is about architecture-level thinking:

Bonus Skills

Specialised Deep Learning: Experience with multimodal alignment or large-scale pre-training and foundation models.
Advanced Domains: Specific knowledge in geometric deep learning or physical signal processing.
Academic Track Record: A history of publications in top-tier venues such as NeurIPS, ICML, or equivalent conferences.
Unconventional Backgrounds: Experience working with “exotic” signals or unique data systems that demonstrate deep thinking and adaptability.

Beyond Technical Skills:

While technical excellence is critical, we place equal importance on how we work together. We believe the best teams are built on:

Understanding Failures

Why do standard methods fail on irregular, noisy, or scarce data? How can we adapt?

Pre-Training Strategy

Develop methods tailored to physical signals (small samples, irregular structures).

Multimodal Alignment

Bridge gaps between signals and find the right inductive biases for physical systems.

End-to-End Ownership

If the core is scientific exploration (experimental and evaluation design), note that as a member of a small team, you’ll own experiments from hypothesis to deployment and contribute to engineering (pipelines, infrastructure).### Requirements

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

We are striving for honesty, kindness, and fairness. We value people who treat others with dignity and foster an environment where everyone feels heard.

Open Communication & Humility

Great ideas come from collaboration. We look for teammates who listen actively, communicate clearly, and approach challenges with self-awareness and humility.

Psychological Safety & Camaraderie

We strive to create a space where people feel safe to take risks, ask questions, and grow.- Application review

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.