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

About the role

Liquid AI builds general-purpose AI systems that run efficiently across various deployment targets, including data center accelerators and on-device hardware. We collaborate with enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. Our team is growing rapidly, seeking exceptional individuals to join us in achieving our goals.

Opportunity

This role offers a unique opportunity to integrate cutting-edge vision-language models with real-world deployment challenges. You will be responsible for adapting, evaluating, and shipping these models for some of the world's largest enterprises, while also contributing to the development of Liquid's core multimodal models.

What We're Looking For

• 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.

Experience

• 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.

Nice-to-Have

• Experience with visual grounding, document understanding, OCR, or video understanding tasks.
• Experience contributing to shared or general-purpose multimodal post-training infrastructure.
• Prior exposure to customer-facing or applied ML delivery environments.
• Familiarity with alignment or RL techniques beyond basic supervised fine-tuning in the multimodal setting.

Success

• Independently owns and delivers enterprise VLM post-training projects with minimal oversight.
• Trusted by customers as the technical owner, demonstrating strong judgment and delivery quality on multimodal workloads.
• Makes durable contributions to Liquid's general-purpose multimodal post-training pipelines by feeding applied learnings back into baseline model development.

What We Offer

• Real ML work: Fine-tunes vision-language models, generates multimodal data, and ships solutions, directly impacting model development.
• Compensation: Competitive base salary with equity in a unicorn-stage company.
• Health: Pays 100% of medical, dental, and vision premiums for employees and dependents.
• Financial: Offers 401(k) matching up to 4% of base pay.
• Time Off: Unlimited PTO plus company-wide Refill Days throughout the year.