Orchestrate the production and delivery of scientific AI components across our research squads, creating the execution framework that transforms our scientific ambitions into concrete, interoperable, and reusable deliverables.

Squad orchestration and coordination

• Work with the CEO and Business Analyst to translate scientific use cases into squadized execution plans (objectives, tempo, milestones, trade-offs)

• Coordinate squads throughout the AI component development cycle: synchronization across domains (neuroimaging, data infra, ML), tempo management, feedback facilitation

• Actively manage inter-squad dependencies, risks, and decisions, maintaining the balance between scientific exploration and robust execution

Delivery process structuring

• Establish and maintain a common delivery framework: documentation, demo formats, tracking tools, definition of “delivered capability”

• Structure a cross-functional delivery process including cadence, validation milestones, use case prioritization

• Establish and stabilize the “Use Case → Squad → Capability → Demo” cycle with regular feedback and clear decisions

Tracking and management

• Ensure incremental delivery of foundational components (pipelines, modules, frameworks) with precise maturity tracking

• Facilitate execution rituals adapted to R&D (lightweight sprint cycles, progress reviews, synchronization)

• Identify and resolve operational blockers, propose process adjustments

Core experience and skills

• 4-8 years of experience managing complex projects/programs, ideally in innovation, AI, or deeptech

• Experience in strategy or transformation consulting with strong exposure to technological or scientific environments

• Demonstrated ability to operate in interdisciplinary, high-uncertainty contexts with operational clarity requirements

Methodological skills

• Proficiency or strong appetite for agile methods adapted to R&D (scientific Kanban, component-oriented roadmap, use-case scoping)

• Comfort with modern collaboration tools (Notion, GitHub, Miro, spreadsheets, roadmap visualization)

• Ability to create structure in ambiguity without constraining scientific exploration

Personal qualities

• Strong autonomy and initiative within a structured framework

• Excellent interpersonal and diplomatic skills (influence without hierarchical authority)

• Comfort working with senior scientific profiles (PhDs, research leads)

• Intellectual curiosity for science and technology (no need to be an engineer, but genuine interest required)

Nice to have

• Experience in an AI lab, deeptech startup, or organization applying research to technology

• Familiarity with ML issues, neuroscience, or scientific data (EEG, MEG, etc.)

• Prescreen with (Head of People)

• Hiring Manager screen (CEO)

• Onsite interview