Datology automatically curates model training data. Train faster, achieve better performance, and reduce costs with enterprise-grade data curation. Backed by Felicis and Amplify.
About the role
Every hire changes the trajectory of the company, and the people we need are some of the most sought after in AI. We're looking for a Senior Technical Talent Partner who is a builder at heart. You'll own our most critical research and engineering searches end to end, from role definition and talent mapping through assessment and close. You'll work side by side with our founders and technical leaders as a true thought partner, shaping who we hire and how we hire them. You'll report to our Head of People. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 5+ years of technical recruiting, with meaningful time at a startup or high-growth tech company
- A proven track record of hiring strong technical talent
- Strong sourcing instincts and the creativity to reach people who are not on the market
- Enough technical fluency to hold credible conversations with engineers and researchers
- Confidence advising hiring managers and pushing back when it serves the outcome
- Experience closing complex offers, including equity and competitive situations
More about this role
Models are what they eat. But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy.
At DatologyAI, we’ve built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models. Training on curated data can dramatically reduce training time and cost ( 7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment. For more details, check out our recent research on synthetic data scaling ( BeyondWeb ) and pretraining with domain-specific data ( The Finetuner’s Fallacy ).
We raised a total of $57.5M in two rounds, a Seed and Series A. Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff...
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