Research Scientist

Prior Labs GmbH; New York (NY); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Healthcare

Transportation benefits

Fitness stipend

Team lunches

Company offsites

Equity compensation

Relocation bonus

Relocation assistance

Job summary

Prior Labs GmbH in New York is seeking a Research Scientist to advance foundation models for structured data and related areas. You will design and run experiments, publish results, and collaborate with a world-class team to build scalable, reliable models.

You will contribute to pushing data size and context limits, explore multimodal approaches, and tackle time series, relational learning, and causal-inference challenges in a fast-paced frontier AI lab.

Qualifications

• Track record of original research contributions (publications, open-source work, benchmarks)
• Strong experience developing and analyzing ML models; able to implement/train methods in PyTorch
• Solid understanding of training dynamics, generalization, and common failure modes in DL systems
• Excellent engineering fundamentals and strong Python skills; write high-quality research code
• Intellectual honesty and constructive collaboration; take feedback seriously and adapt approaches

Responsibilities

• Develop foundation-models for structured data and related areas.
• Design experiments, run analyses, and publish results.
• Collaborate with research engineers to turn promising ideas into reliable models.
• Push limits on dataset size, context length, and training/inference efficiency.
• Explore multimodal data, time series, relational learning, and causal inference.

Skills

PyTorch

Python

Research experience

Experimental design

Collaboration

Tools

Git

Linux

Job description

Pioneer the future of tabular foundation models

Join us in our mission to help humanity making better decisions.

Quality output built on mutual respect and collaboration.

Undogmatic

High integrity

Our company actions reflect our values. Build technology that serves humanity.

Lead by example

Show don't tell. Model the behavior and standards we expect from others.

Mission-driven

Solve real problems with structured data. Build what matters.

Locations

Berlin

Where tech, culture, and creativity collide. Push the boundaries of AI in the city that never stops reinventing itself.

Where academic excellence meets alpine air. Advance AI in a city built around nature and world-class research.

New York

Where relentless energy meets AI innovation. Build at the pace of the city that never stops.

The epicenter of tech innovation. Surrounded by companies shaping the future of AI.

Comprehensive benefits including healthcare, transportation, fitness, team lunches & company offsites

Competitive compensation package with meaningful equity

30 days of paid vacation + public holidays

Seamless relocation including relocation bonus

Work with state-of-the-art ML architecture, substantial compute resources and with a world-class team

Join Prior Labs

Do you want to push the boundaries of foundation models in tabular data?

Research Scientist, Foundation Model

Employment Type

Full time

Location Type

On-site

Department

Who we are

Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched, until now. What LLMs did for language, we're doing for tables.

In July 2026, less than 18 months after our €9M pre-seed, we joined SAP as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.

About the role

We’re hiring researchers to advance foundation models for structured data, with particular interest in structured data, in-context learning, meta-learning, time series and forecasting, relational learning, causal inference, and recommender systems. We welcome deep expertise in any one of these areas, as well as broader foundation-model research experience.

You’ll develop models and learning methods that generalize across datasets and tasks. We’re especially interested in researchers who can bring insights from their field into the design, training, and evaluation of foundation models.

Structured data presents challenges that language and vision architectures don’t solve out of the box. Tables lack a shared vocabulary across datasets; time series introduce temporal dependencies; relational data connects information across tables and entities. TabPFN demonstrated what foundation models can achieve for tabular prediction, and many fundamental research questions remain.

At Prior Labs, Research Scientists drive the core model agenda. You’ll help identify worthwhile research questions, design experiments that distinguish between competing explanations, and take ownership of projects through to publications and model releases. You’ll implement and run your own experiments, interpret the results, and work closely with research engineers to turn promising ideas into reliable models.

The problems you could work on include:

Pushing the limits of dataset size and context length while improving training and inference efficiency.

Building multimodal models that combine tabular data with text and other modalities.

Designing architectures and learning methods for time series, forecasting, and anomaly detection.

Developing relational models that learn across multiple related tables.

Investigating how foundation models can support causal inference and estimation of intervention effects.

Extending foundation-model methods to recommender systems.

Your focus will depend on your strengths and our research priorities. You don’t need expertise across all of these areas or prior experience with TabPFN.

What we’re looking for

A track record of original research contributions, demonstrated through publications at leading venues in your field, influential open-source work, benchmarks, or deployed methods. We also welcome earlier-career candidates with exceptional academic or independent technical work. We care about your contribution and the quality of the work.

Strong experience developing and analyzing machine learning models, with the ability to implement, train, and debug your own methods in PyTorch.

Solid understanding of training dynamics, generalization, and common failure modes in deep learning systems.

Strong experimental judgment: you choose appropriate baselines, investigate unexpected results, and distinguish robust improvements from evaluation artifacts.

Excellent engineering fundamentals and strong Python skills, with a track record of writing high-quality research code.

Intellectual honesty and constructive collaboration: you explain your reasoning, acknowledge limitations, take feedback seriously, and change your approach when the evidence calls for it.

Nice to have

Research experience in tabular data, time series, relational learning, causal inference, or recommender systems. Depth in one area is valuable.

Experience at an early-stage startup or research lab that regularly releases models or systems.

Contributions to open-source ML libraries or tools.

Experience with model scaling, distillation, inference optimization, or efficient architectures.

Life at Prior Labs

You'll work alongside researchers and builders who hold themselves to a very high bar - in the quality of their work and in how they work with each other. We move fast and still take the time to do things right.

Our teams are based in Berlin, Freiburg, and New York - when you're working on something as hard as TabPFN, being in the same room matters. But great people come from everywhere, and in exceptional cases we're open to remote, which usually means frequent travel to one of our offices. Wherever you're based, the whole company comes together regularly for offsites to build and celebrate together.

Our Commitments

The best products and teams are built by people with a wide range of perspectives and backgrounds. We welcome applications from all identities and walks of life - especially if you've ever felt discouraged by "not checking every box" - and provide equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are.