Research Engineer, Multimodal Data

The role

You will own the visual understanding roadmap to make petabytes of video queryable by content. You will train, fine-tune, and evaluate vision and multimodal models to build rich datasets for customer models.

What you will do

• Own the visual understanding roadmap end-to-end
• Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and CNNs
• Drive down per-clip annotation cost through model selection, distillation, batching, and decode pipelining
• Build rich, queryable datasets by designing taxonomies and instrumenting quality
• Partner with dataloading and storage teams
• Work directly with researchers at partner labs

Required qualifications

• Strong familiarity with modern vision and multimodal models including convolution nets, VLMs, VQA, and embeddings
• Experience running these models at scale on real video and sensor data for perception tasks
• Background from a perception team at a self-driving, robotics, or visual-data company or equivalent depth from a research lab
• Comfortable with cloud infrastructure and large-scale data processing
• Experience shipping jobs that run on thousands of GPU-hours of video

Nice to have

• Experience training and evaluating vision or multimodal models
• ML/AI research background including papers, citations, or a research org
• Worked on embeddings, retrieval, or content-aware search at scale
• Experience designing labeling taxonomies or running annotation programs

Benefits

• Competitive comp and meaningful startup equity
• Catered lunches and dinners for SF employees
• Commuter benefit
• Team-building events and poker nights
• Health, vision, and dental coverage
• Flexible PTO
• Latest Apple equipment
• 401(k) plan with match

About the company

Eventual is building data curation for Physical AI by processing video and sensor data at petabyte scale. The small team is from AWS, Lyft and Tesla and is backed by Felicis, CRV, Y Combinator and the co-founders of Databricks and Perplexity.