AI Solutions and Capabilities Sigma Nova
Foundation models for scientific and industrial data.
CDI
Paris
Télétravail fréquent
Salaire : non spécifié
> 7 ans
Bac +5
Les compétences que l’on cherche
Aptitudes techniques
Prototypage
Collaboration et travail d'équipe
Gather
Foundation
+2
Votre nouvelle entreprise
16 collaborateurs
Intelligence artificielle / Machine Learning
Expert AI: foundation models for the data that runs science and industry
We define Expert AI as foundation models built for scientific and industrial data: continuous and irregular time signals, spatiotemporal fields, multimodal scientific recordings, time-stamped event logs from real organizations. The kind of data that doesn’t look like text, doesn’t fit transformer assumptions, and rarely leaves the institutions that hold it. This isn’t a fine-tuning problem. It requires architectures designed for the data, infrastructure designed for how the data lives, and a tech stack of AI components purpose-built to assemble foundation models for new domains.
General-purpose AI was never built for science and industry. The data is sparse, noisy, distributed, and governed by strict rules. It demands expert intelligence: models that understand the structure of a domain, not just the surface of its data.
Our approach is co-foundation with domain experts: AI and domain science evolving together. We build domain-specialised foundation models trained to master a field’s data, constraints, and logic from the ground up. We work hand in hand with the scientists, clinicians, and engineers who know the domain best. Our models learn from distributed sources through decentralised architectures, keeping sensitive data where it belongs.
We anchor in brain and neuroimaging, the most complex signal of all and the hardest class of expert data we know how to characterize. In parallel, our cross-domain transfer programme works on extending the same components and methods to open new application domains.
We invest in demonstrators that turn research into tangible proof, working with partners on concrete use cases that test our models in real conditions and inform the next iteration of the science.
Our team finished 2nd out of 1,183 in the NeurIPS 2025 EEG Challenge, and we publish at Tier-1 AI conferencesincluding NeurIPS, ICML, and CVPR. This dual focus, rigorous science and applied impact, defines our approach.
Vos missions au quotidien
Role Overview
Sigma Nova Science develops foundation model architectures for complex scientific domain applications. We are transitioning our core research assets into scalable, commercial AI capabilities that enterprise clients can evaluate, operate, and buy.
In this role, you will lead the bridging work between research initiatives and early client opportunities. You will frame capability definitions, scope client demonstrators, structure upstream technical validations, and package solutions. Operating with high autonomy and agility, you will co-construct offerings alongside cross-functional teams (R&D, Strategy, Partnerships, Ops) and collaborate directly with the CEO to shape early offer positioning and strategic roadmap priorities.
Role Focus & Collaborative Alignment
Core Focus & Framing: You lead capability definition, solution specifications, offering packaging, pricing options, and demonstrator design.
Cross-Functional Co-Construction: You partner continuously with Partnerships & GTM on client feedback and account expansion, Research & Tech Dev on reusability and feasibility, Strategy on value propositions and market alignment, and Programs & Operations on tracking and milestone delivery.
CEO Collaboration: You work directly with the CEO—engaging in strategic and scientific brainstorming—to align on pricing recommendations, domain sequencing, and collaborative project commitments.
Key Responsibilities
AI Capabilities & Offer Definition (30%)
Productization & Structuring: Translate abstract research assets and codebase components into modular, functional capabilities.
Qualification & Sequencing: Define qualification criteria (data availability, partner engagement, ROI, timeline) to prioritize exploration across domains.
Packaging & Pricing: Build capability specifications, licensing ideas, and pricing proposals alongside Strategy and IP counsel for executive review.
Reusability Audits: Partner with Research & Tech Dev to distinguish core platform building blocks from custom client work.
Demonstrators & Technical Validation (25%)
Validation Protocols: Define scope, success criteria, and test protocols for client demonstrators based on strategy use-case dossiers.
Rapid Prototyping: Build lightweight proofpoints and fast prototypes (including vibe coding) to test technical hypotheses quickly.
Feedback Loops: Lead test-learn-pivot cycles on demonstrator outcomes to continuously refine offer specifications.
Upstream GTM & Technical Account Reference (25%)
Technical Pre-Sales: Assess client data maturity, evaluate data processing complexity, and structure technical expansion pathways for enterprise accounts.
Proposal Delivery: Draft technical sections of client proposals and execute feasibility sign-offs prior to external commitments.
Field Intelligence: Gather field objections, client needs, and reuse signals to inform central product and technical planning.
Discovery & Ecosystem (20%)
Frontier Scanning: Track scientific literature and emerging AI developments to keep the company aligned with state-of-the-art research.
Collaborative Projects: Represent the company in industrial/academic consortia and contribute technical sections to French and European grant proposals.
Exploratory Outreach: Engage technical stakeholders in new domains to build long-term pipeline opportunities.
Ce que l’on cherche
Experience: 7–8+ years operating at the intersection of AI research, deep-tech engineering, and commercial solutions strategy.
Scientific & Technical Depth: Advanced degree or equivalent depth in science/engineering; ability to engage as an intellectual peer with PhD/HDR-level scientific leaders and research teams.
Solutions & Value Framing: Strong instincts for translating abstract technical codebases into customer-facing capabilities with clear ROI.
Hands-On Technical Ability: Comfortable prototyping, writing code, and conducting technical feasibility evaluations directly.
Avantages
Nos étapes de recrutement
Recruiter prescreen
Business/Product discussion
Onsite interview (brainstorming, culture fit..)
