Data Labeling Lead

Spore.Bio; Paris; Hybride; EUR 65 000 - 90 000; Plein temps

Avantages offerts par ce poste

Remote work budget

Gymlib membership

Health insurance (Alan)

Swile card

Team events

Résumé du poste

Spore.Bio in Paris is seeking a rigorous Data Labelling Lead to scale the annotation engine at the heart of our AI pipeline. You will translate scientific needs into annotation strategies and build scalable, well-governed workflows as the team grows.

You will lead the annotation end-to-end, mentor the team, and ensure high-quality data for microbiological detection models, bridging scientific domain knowledge with ML production requirements.

Qualifications

• MSc or engineering degree in a related field.
• 5 years in data annotation or ML data operations.
• Strong understanding of ML data needs and annotation impact.

Responsabilités

• Define annotation guidelines and schemas.
• Own end-to-end annotation workflows and QC.
• Manage external annotation vendors and delivery.
• Track progress and report to stakeholders.
• Line-manage and mentor QC Specialist.

Connaissances

Data annotation

ML data ops

Python scripting

Labeling platforms

Dashboard/KPI tracking

Mentoring

Project management

Formation

MSc or engineering degree in a related field

Outils

CVAT

Label Studio

Labelbox

Scale AI

Description du poste

Spore.Bio is a deeptech start-up born in 2023, building a new paradigm in the quality control systems in Food&Beverage, Cosmetics, and Pharmaceutics factories.

After spending a lot of time in factories environments, we saw the pain it was to make sure products were safe. Traditional quality control has heavy constraints and long waiting times. To change that, we decided to build Spore.Bio, a new generation of microbiological testing.

Our team is dedicated to bringing technology and developing a cutting-edge solution, based on advanced optical and deep-learning technologies, to detect bacterial contamination within seconds.

Spore Bio is seeking a rigorous and driven Data Labelling Lead to scale and own the annotation engine at the heart of our AI pipeline. In this role, you will be the operational and strategic backbone of how we turn raw optical imaging data into high-quality training data for our microbiological detection models. You will bridge the gap between scientific domain knowledge and ML production requirements, designing the workflows, standards, and systems that determine the quality of every model we train and the reliability of what we deliver. Join our team of R&D experts, engineers, and data scientists, and play a foundational role in building the future of industrial microbiology.

About the role

Our core mission is to develop AI-powered detection of microbiological entities on optical data. High-quality, reproducible annotations are the foundational input to every model we train. The Data Labelling Lead will own this function end-to-end: translating scientific needs into annotation strategies, ensuring process rigor, and building a scalable, well-governed workflow as the team grows. You will lead, operate, and continuously improve the annotation engine that powers our ML models.

Key Responsibilities:

Process & Strategy

• Define and maintain annotation guidelines and labelling schemas for optical/microbio datasets
• Own end-to-end annotation workflows: task scoping, assignment, QC checkpoints, delivery, and feedback loops
• Anticipate bottlenecks and drive continuous optimisation as the team and scope scale

Demand & Project Management

• Intake and prioritise annotation demand from ML and R&D teams; translate into sprint-level plans
• Own relationships with external annotation vendors; ensuring excellence in quality and delivery
• Track progress and report delivery status to stakeholders
• Own the annotation dashboard and define KPIs; elevate quality or capacity risks proactively
• Line-manage and mentor the QC Specialist and any future team member

About you

• MSc or Engineering degree in Biomedical Engineering, Bioinformatics, Computer Vision, Data Science, or related field
• 5 years in data annotation, ML data operations, or a closely related role
• Strong understanding of ML model requirements and how annotation quality affects model performance
• Exposure to biomedical microscopy, biophotonics, or life-science imaging strongly preferred
• Proven track record managing annotation/data pipelines at scale (10k+ samples or equivalent)
• Proficiency with Python scripting for workflow automation and data QC
• Experience translating complex scientific or technical requirements into clear annotation guidelines
• Familiarity with data labelling platforms (CVAT, Label Studio, Labelbox, Scale AI, etc.)
• Experience designing and maintaining annotation dashboards and KPI tracking systems

Soft Skills & Mindset

• Strong project management capabilities; able to handle multiple concurrent workstreams
• Excellent communicator bridging scientific domain experts and ML engineers
• Systematic thinker with a continuous-improvement mindset
• Comfortable with ambiguity in early-stage, research-driven environments
• Proven ability to mentor and lead specialists
• Work in an innovative and rapidly growing startup.
• Participate in exciting and impactful projects.
• Evolve in a collaborative and stimulating work environment.
• Opportunities for professional development and continuous training.

What we offer

We believe that flexibility and trust are important parts of a company. Our work environment reflects this thanks to:

• Flexible remote: If you live in Paris, you can work from our office or from your place with no constraints.

On top of that, we offer many perks such as:

• A budget for remote work equipment
• A Gymlib subscription for you to stay in shape wherever you are
• Premium health insurance (Alan in France)
• A Swile card for your meals, if you are based in France
• Frequent team events and in-person gatherings every quarter!

Recruitment process

• Fit interview (~30 min): A call to get to know each other, your experience, what drives you, and what you're looking for. It's also your chance to ask anything about Spore.Bio and the role.
• Technical case study (take-home+ presentation): A hands-on challenge reflecting the kind of problems you would face: annotation workflow design, demand prioritisation, quality system setup, and handling ambiguous edge cases at scale. We care about your reasoning and your instincts, not textbook answers.
• On-site interview Lab visit + Founders meeting: You will meet the founders, visit the lab, and see Louis in action.