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Member of Technical Staff - ML Research Engineer, Data

Liquid AI· Research & Engineering
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Hybrid (San Francisco), Hybrid (Boston)Full-timePosted 11mo ago

About the role

ML research-minded data engineer role focused on building and evaluating data pipelines for Liquid Foundation Models, emphasizing data quality, synthetic data generation, and modality-specific data needs across pre-training, post-training RL, vision-language, audio, and multimodal.

Responsibilities

  • Build and maintain data processing, filtering, and selection pipelines at scale
  • Create pipelines for pretraining, midtraining, SFT, and preference optimization datasets
  • Design synthetic data generation systems using LLMs, structured prompting, and domain-specific generators
  • Design and run evaluations and ablations to measure dataset's impact on model performance
  • Monitor public datasets across text, vision, and audio domains
  • Collaborate with pre-training, vision, and audio teams on modality-specific data needs

Qualifications

  • Strong Python skills with the ability to quickly comprehend problems and translate them into clean, working code
  • Solid ML fundamentals: experience training, evaluating, and iterating on models (PyTorch preferred)
  • Track record of learning new technical domains quickly
  • 3+ years relevant experience with an M.S., or 1+ year with a Ph.D. (5+ years with a B.S.)

Skills

Required:PythonMachine Learning fundamentalsDomain learning abilityData pipeline design

Benefits

  • Impact at scale: Your pipelines directly determine model quality across all of Liquid's foundation models.
  • Competitive base salary with equity in a unicorn-stage company
  • We pay 100% of medical, dental, and vision premiums for employees and dependents
  • 401(k) matching up to 4% of base pay
  • Unlimited PTO plus company-wide Refill Days throughout the year
Last updated Oct 21, 2026. Interested? Apply on the company site.
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About Liquid AI

Build efficient general-purpose AI at every scale.

We build efficient general-purpose AI at every scale. Liquid Foundation Models (LFMs) are a new generation of generative AI models that achieve state-of-the-art performance at every scale, while maintaining a smaller memory footprint and more efficient inference.

AIEdge AIFoundation ModelsGenerative AIliquid-ai-recruiting-target
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