Startups · AI

Member of Technical Staff - Post Training, Applied (Vision)

Liquid AI · San Francisco · Remote

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About Liquid AI

Liquid AI builds efficient Liquid Foundation Models (LFMs) for on-device, edge, and cloud AI with low latency, privacy, and hardware-aware deployment.

About the role

This is a rare chance to sit at the intersection of frontier vision-language models and real-world deployment. You'll own applied post-training work for VLMs end-to-end for some of the world's largest enterprises, while still contributing directly to Liquid's core multimodal model development.

What they're looking for

  • Hands-on experience with data generation and evaluation for VLM or multimodal post-training
  • Experience training or fine-tuning vision-language models using SFT, preference alignment, and/or RL
  • Strong intuition for visual data quality, annotation design, and multimodal evaluation
  • Familiarity with vision encoders, image-text architectures, and how visual representations interact with language model backbones
More about this role

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

This is a rare chance to sit at the intersection of frontier vision-language models and real-world deployment. You'll own applied post-training work for VLMs end-to-end for some of the world's largest enterprises, while still contributing directly to Liquid's core multimodal model development.

Unlike most roles that force a trade-off between customer impact and foundational work, this role gives you both: deep ownership over how vision-language models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's multimodal post-training stack.

If you care about visual understanding, data quality, evaluation, and making VLMs actually work in production, this is a chance to shape how applied multimodal AI is done at a foundation model company.

Takes...

Read the full posting on Liquid AI's site ↗

Applied ML

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