Member of Technical Staff (gn) Physical AI

FoodLabs; Paris; Sur place; EUR 120 000 - 180 000; Plein temps

Résumé du poste

FoodLabs in Paris is seeking a technical leader to shape data infrastructure for frontier AI and robotics. You will design and build large-scale data ingestion, storage, and processing systems that power real-world datasets with strict provenance and privacy.

You will own the architecture, work directly with researchers and customers, and recruit the first engineers. This founder-friendly role offers meaningful equity and a chance to influence the direction of the company from first principles

Qualifications

• Built large data systems and distributed pipelines.
• Experience with geospatial, imagery, or robotics data.
• Created products or platforms others rely on.
• Active hands-on coding and debugging.
• Ability to test views quickly and adapt.
• Capable of architectural decisions and raising the bar.
• Founder mindset with ownership of outcomes.
• Location: Paris preferred; Europe possible with time in Paris; English required; French is a plus.

Responsabilités

• Capture data at scale from phones, cameras, wearables, and edge devices.
• Own the data layer: storage, geo-indexing, versioning, verification, search, API.
• Build ML enhancements for anonymisation and quality scoring.
• Develop contributor SDKs and onboarding tooling.
• Define infrastructure compute: edge vs cloud, GPU pipelines.
• Hire and mentor the first engineers, set engineering bar.
• Set architecture and roadmap aligned with customer needs.
• Ownership and equity as a founder.

Connaissances

Infrastructure depth

Physical-world data

0 to 1

Hands-on coding

Judgment under uncertainty

Technical leadership

Founder mindset

Location Paris

Description du poste

FoodLabs is Europe's leading early-stage VC and venture studio, backing founders who are shaping the future of the food, health, and climate value chains. We build companies from zero, putting capital, our own team, and hands-on operating time behind a founding team before there's a product or even a name. The studio's work spans wherever a market and the data or infrastructure underneath it are broken enough to justify building the fix from scratch.

About The General Data Corporation

The General Data Corporation is the physical-world data infrastructure company for frontier AI and robotics teams. We collect observations from cameras and sensors, then turn them into trusted datasets for training and evaluation. Our platform handles ingestion, privacy, provenance, quality control, and delivery at scale.

As embodied AI and world models move beyond internet data, they need a reliable view of the real world and how it changes. We are building that record: continuously updated, machine-readable, and rights-aware. Our goal is to become Europe’s data neoprime for physical-world intelligence.

Why this is a unique opportunity

AI has absorbed the internet, but it still has a poor view of the physical world. While the world is running out of unique, publicly available data for further training, we believe extraordinary value will be created by businesses that generate unique datasets. Teams building robots and world models need fresh, geo-referenced data from real places, with clear provenance and usage rights. We are building that infrastructure - GDPR compliant.

We have collected more than 1M observations of Paris, built an open-source driving-scene model and developed a GDPR-native stack with a former Google Street View Data Protection Officer. The next step is to turn that working system into infrastructure that frontier AI and robotics teams depend on. Think Hugging Face for physical-world data, where every observation is a commit and every coordinate has a change history.

You would join Louai Allani as the second founding pillar of the company, with ownership of the technical product from first principles through scale. Refining the direction of the company and validating the first steps to secure additional funding from our Fund.

About the role

• Capture at scale: Build ingestion for phones, dashcams, wearables, and edge devices. Scale volume while reducing cost and preserving provenance, quality and privacy.
• The data layer: Own storage, geo-indexing, temporal versioning, verification, search, permissions and API delivery. This is the core product.
• ML and data quality: Build anonymisation, attribute extraction, deduplication, quality scoring and change detection. Use ML where it improves the system, not where deterministic methods work better.
• Contributor platform: Build the SDKs, onboarding and tooling that let the collection network scale without manual operations.
• Infrastructure and compute: Own how data moves, where it is processed and what runs at the edge versus in the cloud. Build the media, distributed compute and GPU infrastructure behind it.
• Engineering team: Hire the first engineers, set the technical bar and stay close to the code.
• Technical direction: Own the architecture and technical roadmap. Work directly with customers and researchers to understand what the system needs to become.
• Founder ownership: Meaningful equity, equal say in what we build and responsibility for the outcome.

What you need to succeed

• Infrastructure depth: You have built large data systems, distributed pipelines, storage or retrieval infrastructure and know where they fail under load.
• Physical-world data: Experience with geospatial, imagery, video, sensors, mapping, robotics or autonomous systems.
• 0 to 1: You have built a product, platform, research system or open-source project that others relied on.
• Hands-on: You still write code, debug systems, and implement critical paths yourself.
• Judgment under uncertainty: You form strong views, test them quickly and change course when the evidence says you should.
• Technical leadership: You can make architectural decisions, explain them clearly and raise the engineering bar as the team grows.
• Founder mindset: You care about the outcome, not the title. You are comfortable owning technical, product and operational problems.
• Location: Paris preferred. Europe possible with regular time in Paris. English required; French is a plus.

Come build the infrastructure between AI and the physical world with us!