Member of Technical Staff - Foundational Model Data

Liquid AI, an MIT spin-off, is a foundation model company headquartered in Boston, Massachusetts. Our mission is to build capable and efficient general-purpose AI systems at every scale.
Our goal at Liquid is to build the most capable AI systems to solve problems at every scale, such that users can build, access, and control their AI solutions. This is to ensure that AI will get meaningfully, reliably and efficiently integrated at all enterprises. Long term, Liquid will create and deploy frontier-AI-powered solutions that are available to everyone.
We are seeking a highly skilled Member of Technical Staff, Foundation Model Data to play a critical role in our foundation model development process. This role focuses on consolidating, gathering, and generating high-quality text data for pretraining, midtraining, SFT, and preference optimization.

Key Responsibilities

• Create and maintain data cleaning, filtering, selection pipeline than can handle >100TB of data.
• Watch out for the release of public dataset on huggingface and other platforms.
• Create crawlers to gather datasets from the web where public data is lacking.
• Write and maintain synthetic data generation pipelines.
• Run ablations to assess new dataset and judging pipelines.

Required Qualifications

• Experience Level: B.S. + 5 years experience or M.S. + 3 years experience or Ph.D. + 1 year of experience.
• Dataset Engineering: Expertise in data curation, cleaning, augmentation, and synthetic data generation techniques.
• Machine Learning Expertise: Ability to write and debug models in popular ML frameworks, and experience working with LLMs.
• Software Development: Strong programming skills in Python, with an emphasis on writing clean, maintainable, and scalable code.

Preferred Qualifications

• M.S. or Ph.D. in Computer Science, Electrical Engineering, Math, or a related field.
• Experience fine-tuning or customizing LLMs.
• First-author publications in top ML conferences (e.g. NeurIPS, ICML, ICLR).
• Contributions to popular open-source projects.