Research Scientist: Foundation Models for Structured Data

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

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.