Hiring: Member of Technical Staff - Post Training, Applied (Vision) Company: Liquid AI Location: San Francisco, CA Job Posted Time: 2026-09-10 12:46:35 Target Skills & Keywords : Fine-tuning, Reinforcement Learning About the job Experience: •Applied hands-on capability in data generation and evaluation for VLM or multimodal post-training. •Strong intuition for visual data quality, annotation design, and multimodal evaluation. •Operational familiarity with vision encoders, image-text architectures, and how visual representations interact with language model backbones. Required Skills: •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 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. •Act as the technical owner for enterprise customer VLM post-training engagements. Qualifications: •Prior exposure to customer-facing or applied ML delivery environments. •Operational familiarity with alignment or RL techniques beyond basic supervised fine-tuning in the multimodal setting. •What Success Looks Like (Year One) •Independently owns and delivers enterprise VLM post-training projects with minimal oversight. •Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality on multimodal workloads. •Has made durable contributions to Liquid's general-purpose multimodal post-training pipelines by feeding applied learnings back into baseline model development. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!