I'm partnering with a ~$9B frontier AI company building next-generation embodied AI and world models.
They're looking for an exceptional Sernior Research Engineer to tackle one of the hardest problems in frontier AI:
Given massive amounts of multimodal data and compute, how do you determine which data will actually make the next model better - before you train it?
You'll work across:
- Data quality, valuation, selection & curation
- Massive-scale image, video & multimodal datasets
- Dataset enrichment and evaluation
- Understanding how training data impacts downstream model performance
- World models / frontier multimodal AI
- Technical strategy across research, data & model teams
This is a highly senior IC role with significant technical influence across the organization, not simply a data pipeline or infrastructure position.
Ideal background:
- Experience working with multimodal, image, video, robotics or other large-scale ML datasets
- Deep understanding of data quality and its relationship to model performance
- Experience operating at genuine frontier-model / production scale
- Strong cross-functional technical leadership and judgment
- Comfortable solving ambiguous, open-ended research and engineering problems
Autonomous driving experience is NOT required. We're particularly interested in people from frontier AI labs, multimodal/video foundation-model teams, robotics, and other organizations solving data problems at enormous scale.
馃搷 Bay Area / London + flexibility for exceptional candidates
馃挵 Highly competitive compensation + meaningful equity
If you've worked on understanding what makes data valuable for training frontier models, I'd love to hear from you.