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Prior Labs is building foundation models that understand tabular data, the backbone of science and business. You'll be among the engineers developing an entirely new class of AI models. Our Research Engineers are core members of the science team, contributing to architectural design while ensuring our models scale to the next order of magnitude. As an early team member, you'll have significant technical ownership and the opportunity to grow into a leadership position as we scale.
Job Responsibility:
Build and improve training pipelines for large-scale tabular foundation models
Design modular architectures that support rapid experimentation
Optimize training and inference performance
Improve experiment tracking and evaluation systems
Build efficient data processing pipelines for tabular data
Maintain clean, documented codebases that the team can build upon
Design scalable serving architecture for our models
Implement deployment pipelines
Requirements:
Strong engineering fundamentals with excellent Python expertise
Deep experience with ML frameworks, especially PyTorch, Scikit-Learn
Proven track record of implementing and deploying ML systems
Passion for writing clean, maintainable, and well-documented code
Demonstrated interest in foundation models and their real-world applications
Nice to have:
Master's degree or PhD in Computer Science or related technical field
Contributions to open-source projects in related fields
Experience implementing large language models or foundation models
Track record of implementing papers
Background in ML infrastructure and tooling
Experience with distributed training systems
What we offer:
Competitive compensation package in line with industry experience plus meaningful equity
30 days of paid vacation + public holidays
Comprehensive benefits including healthcare, transportation, and fitness
Work with state-of-the-art ML architecture, substantial compute resources and with a world-class team