6 months ago
Responsibilities
- Act as the technical owner for enterprise customer vision-language model post-training engagements
- Translate customer requirements into multimodal post-training specifications and workflows
- Design and execute visual data generation, filtering, and quality assessment processes
- Develop image-text pair curation, annotation, and synthetic data generation pipelines for visual tasks
- Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models
- Design and interpret evaluations for visual understanding, grounding, OCR, document parsing, and other multimodal capabilities
- Feed evaluation learnings into core post-training pipelines and 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 supervised fine-tuning, preference alignment, and/or reinforcement learning
- Strong intuition for visual data quality, annotation design, and multimodal evaluation
- Familiarity with vision encoders, image-text architectures, and interactions between visual representations and language model backbones
- Experience with visual grounding, document understanding, OCR, or video understanding is preferred
- Experience contributing to shared or general-purpose multimodal post-training infrastructure is preferred
- Prior exposure to customer-facing or applied ML delivery environments is preferred
- Familiarity with multimodal alignment or reinforcement learning techniques beyond basic supervised fine-tuning is preferred
Benefits
- 100% employer-paid medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO and company-wide Refill Days
Categories
AI ResearchML Engineering
About Liquid AI
Liquid AI builds general-purpose AI systems that run efficiently from data center accelerators to on-device hardware, emphasizing low latency, memory efficiency, privacy, and reliability. The company partners with enterprises in consumer electronics, automotive, life sciences, and financial services to deploy and benchmark models for real-world workloads. Founded in 2023 out of MIT CSAIL and headquartered in Cambridge, Massachusetts, it is privately held.