Research Scientist Intern (PhD)

Prior Labs; New York (NY); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Mentorship and professional growth

State-of-the-art ML architecture and <

Healthcare, transportation, and gym/福利

Job summary

Prior Labs builds tabular foundation models for structured data at scale. We are a small, high-signal team of engineers and researchers focused on production-ready AI for tables. We seek a PhD‑level candidate with deep ML framework experience (PyTorch, scikit‑learn), strong Python, and work with tabular data or time series.

Publications or OSS contributions are valued indicators of impact. You’ll join a world-class team with mentorship, compute resources, and benefits as we push toward

Qualifications

• Pursuing or holding a PhD in CS, Applied Math, Statistics, Electrical Engineering, or related field.
• Deep experience with ML frameworks, especially PyTorch and scikit-learn.
• Strong Python engineering fundamentals.
• Experience with tabular data or time series.
• Publications at top-tier venues (NeurIPS, ICML, ICLR) or significant open-source contributions.

Skills

PyTorch

Python

Time series data

Tabular data analysis

Publications/OSS contributions

Education

PhD in Computer Science or related field

Master’s student consideration

Tools

scikit-learn

Job description

Who We Are

Foundation models have transformed text and images, but structured data — the largest and most consequential data modality in the world — has remained untouched. Tables power every clinical trial, every financial model, every scientific experiment, every business decision. No one has built a foundation model that truly understands them. Until now. What LLMs did for language, we’re doing for tables. The next modality shift in AI is happening — and we’re hiring the team that makes it.

Momentum

We pioneered tabular foundation models and are now the world‑leading organization in structured data ML. Our TabPFN v2 model was published in Nature and set a new state‑of‑the‑art for tabular machine learning. Since its release, we’ve scaled model capabilities more than 20x, reached 3M+ downloads, 6,000+ GitHub stars, and are seeing accelerating adoption across research and industry — from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical trial decisions with BostonGene. The hardest work is in front of us. We’re scaling tabular foundation models to handle millions of rows, thousands of features, real‑time inference, and entirely new data modalities — while building the infrastructure to deploy them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level.

Our Team

We’re a small, highly selective team of 20+ engineers, researchers and GTM specialists, selected from over 5,000 applicants, with backgrounds spanning Google, Apple, Amazon, Microsoft, G‑Research, Jane Street, Goldman Sachs, and CERN. Led by Frank Hutter, Noah Hollmann and Sauraj Gambhir and advised by world‑leading AI researchers such as Bernhard Schölkopf and Turing Award winner Yann LeCun, we ship fast, create top‑tier research, and hold each other to an extremely high bar.

What’s Next

In 2025, we raised €9m pre‑seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here which makes this an optimal time to join.

Core Areas of Impact

You’ll be among the first scientists collaborating and working on an entirely new class of AI models. 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. We are working on problems such as:

• 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

What We’re Looking For

• 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

Benefits

• Strong mentorship and professional development opportunities
• Work with state‑of‑the‑art ML architecture, substantial compute resources, and a world‑class team
• Comprehensive benefits including healthcare, transportation, and fitness

Life at Prior Labs

We’re a small, ambitious team solving one of the hardest problems in AI, and we’re just getting started. You’ll work closely with world‑class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with. We move fast, we think rigorously, and we take the time to do things right. If you’re excited by hard problems, motivated by real‑world impact, and want to be part of building something that matters, we’d love to hear from you.

Our Commitments

We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That’s why we welcome applications from people of all identities and walks of life, especially anyone who’s ever felt discouraged by “not checking every box.”

We’re committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are.

We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.