About Liquid AI

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.

The Opportunity

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.

What We're Looking For

We need someone who:

• Takes ownership: Owns VLM post-training projects end-to-end, from customer requirements through delivery and evaluation.
• Thinks end-to-end: Can reason across visual data curation, training, alignment, and evaluation as a single system.
• Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory.
• Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed.

The Work

• Act as the technical owner for enterprise customer VLM post-training engagements.
• Translate customer requirements into concrete multimodal post-training specifications and workflows.
• Design and execute visual data generation, filtering, and quality assessment processes, including image-text pair curation, annotation pipelines, and synthetic data generation for visual tasks.
• Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models.
• Design task-specific evaluations for visual understanding, grounding, OCR, document parsing, and other multimodal capabilities. Interpret results and feed learnings back into core post-training pipelines.