Member of Technical Staff - Post Training, Applied (Vision)
Remote • San Francisco • FullTime
Posted 6mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Engineer
About the job
Liquid AI, a rapidly scaling company spun out of MIT CSAIL, is building efficient, general-purpose AI systems for diverse enterprise applications. This role offers a unique opportunity to work at the forefront of vision-language models (VLMs) and their real-world deployment. You will take end-to-end ownership of applied VLM post-training for major enterprises, while also contributing to the company's core multimodal model development. This position allows you to shape the future of applied multimodal AI by focusing on visual understanding, data quality, evaluation, and production readiness.
Responsibilities
- 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 evaluation results and feed learnings back into core post-training pipelines.
- Independently own and deliver enterprise VLM post-training projects with minimal oversight.
- Contribute to Liquid's general-purpose multimodal post-training pipelines by feeding applied learnings back into baseline model development.
Requirements
- 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.
- Experience with visual grounding, document understanding, OCR, or video understanding tasks (nice-to-have).
- Experience contributing to shared or general-purpose multimodal post-training infrastructure (nice-to-have).
- Prior exposure to customer-facing or applied ML delivery environments (nice-to-have).
- Familiarity with alignment or RL techniques beyond basic supervised fine-tuning in the multimodal setting (nice-to-have).
Benefits
- Competitive base salary
- Equity in a unicorn-stage company
- 100% coverage of medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO
- Company-wide Refill Days