ML engineer to own the supplement data infrastructure

Join LabelWise as Co-Founder to own the data infrastructure behind structured supplement intelligence — consumer mobile app + LLM-ready API in the $200B+ market.

Your mission: own the ML stack that turns raw labels into trusted, citable, structured outputs at scale.

What you'll own: (1) Entire ML/AI pipeline (DSPy, VLM inference, OCR-to-extraction) (2) Evaluation infrastructure: CI-gated accuracy per module (3) Data moat scaling 50K+ → 100K+ products without regression (4) AI/ML roadmap, hiring, and technical voice with LLM / health platform partners (5) GPU infrastructure strategy.

Start on contract, convert to co-founder equity when first module ships at target accuracy.

Title flexible: CTO or Chief AI/Data Officer.

We offer co-founder equity, experienced team (€100M+ exits), ZHAW academic collaboration, existing data moat (50K+ products), and remote flexibility.

You bring: 3+ years shipping production ML pipelines, DSPy / MIPROv2 or equivalent, VLM serving (vLLM / TGI / llama.cpp), eval-first discipline, and co-founder mindset.

Ready to build the data layer the next decade of AI health platforms will run on? Send a short email to Andrius at E-Mail auf startupticker.ch ansehen

Project description

LabelWise.org is an AI-powered supplement analysis platform that instantly reveals the true quality, safety, and value behind supplement labels. We're solving the $200B+ supplement market's transparency crisis through our proprietary SQSI algorithm—providing instant label scanning, quality scoring, and personalized recommendations. Our platform grows stronger through user reviews and real-world feedback, creating comprehensive supplement intelligence. Join thousands making more informed supplement choices. Beta launches Aug-Sep'25.