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Prior Labs is building foundation models that understand tabular data, the backbone of science and business. Foundation models have transformed text and images, but structured data has remained largely untouched. We’re tackling this $600B opportunity to fundamentally change how organizations work with scientific, medical, financial, and business data. You'll be among the first scientists collaborating and working an entirely new class of AI models, not just incremental improvements. As an early-stage startup working on foundation models for tabular data, we have countless exciting research ideas and problems to explore - you're sure to find challenges that match your interests and expertise.
Job Responsibility:
Scaling our transformer architectures from 10K to 1M+ samples while maintaining performance
Building multimodal models that combine text and tabular understanding on proprietary data
Developing specialized architectures for time series, forecasting, and anomaly detection
Creating efficient inference methods for production deployment
Researching causal understanding in foundation models
Designing novel approaches for handling multiple related tables
Requirements:
Currently pursuing or holding a PhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a related field (we will also consider exceptional Master's students)
Deep experience with ML frameworks, especially PyTorch and scikit-learn
Strong engineering fundamentals with excellent Python expertise
Experience in data-science and working with tabular data or time series
Publications at top-tier venues (NeurIPS, ICML, ICLR) or significant open-source contributions
What we offer:
Strong mentorship and professional development opportunities
Work with state-of-the-art ML architecture and substantial compute resources