What are the responsibilities and job description for the AI Research Scientist position at Wirestock?
About Wirestock
Wirestock is one of the leading data platforms for ethically sourced multimodal data. We serve some of the world’s top AI labs, including several foundation models, by providing high-quality, fully licensed training datasets. As the AI data landscape undergoes a major shift, we are scaling rapidly to meet rising demand for curated visual data.
Role Description
At Wirestock, we believe that the next leap in AI performance won't come from larger models, but from better data. We are looking for an AI Research Scientist who is obsessed with the Data half of Algorithms. Your role is to be the technical architect of our data lab, identifying what data the world's leading AI labs need before they even realize they need it.
What You Will Own
The Research Frontier: You will be our SOTA scout. You’ll spend your time dissecting the latest models and papers to identify gaps and then design the dataset that fixes it.
Dataset Architecture: You will design the ground truth for multi-modal systems. This means architecting how we use real world data to create datasets that don't just look good, but are mathematically superior for training.
Technical Diplomacy: You will be the peer-level contact for researchers at top-tier AI labs, helping them develop the next big models.
Validation & Prototyping: You will run data experiments using synthetic tools and our global creator network, you’ll build small-scale proof of concept datasets to validate your hypotheses before we scale them to millions of assets.
What You Will NOT Do
Model Training: You are here to build the engine’s fuel, not the engine itself. We leave the model training to our clients so that you can focus 100% of your intellectual energy on data innovation.
Who You Are
The Multimodal Expert: You have a Master’s or Ph.D. in CS/ML or equivalent industry research experience and can talk at length about:
- Generative model architectures
- Dataset bias measurement
- Synthetic vs real data mixing
- Evaluation protocols
- Multimodal alignment metrics
Data-Obsessed: You believe that a perfectly curated 10k-video dataset is worth more than a billion-scale "scrape" of the internet.
The Creative Scientist: You are as comfortable discussing the physics of light and motion as you are writing PyTorch scripts to validate CLIP-alignment.