Eventual
Research Engineer, Multimodal Data
San Francisco, USPosted 3 months ago
Full TimeUS
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Job Description
About Eventual Every breakthrough Physical AI system — humanoid robots, autonomous vehicles, video generation models — is trained on petabytes of video, lidar, radar, and sensor data. But today's data
Key Highlights
- Own the visual understanding roadmap end-to-end: from picking the model family for a customer's taxonomy to landing it in production inference at corpus scale.
- Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and convolutional perception models against customer datasets and benchmarks.
- Drive down per-clip annotation cost — model selection, distillation, batching, decode pipelining — so "annotate every clip in a 10K-hour corpus" stays economical.
- Build the rich, queryable datasets that customers train on: design taxonomies with researchers, instrument quality, version the outputs.
- Partner with the dataloading and storage teams so visual understanding outputs flow into the index and on to the GPU without re-engineering.
Qualifications
Preferred Qualifications
- Experience training vision or multimodal models from scratch (not just calling APIs).
- ML/AI research background — papers, citations, or a research org on your resume.
- Hands-on time with big-data frameworks like Spark, Ray, or Daft.
- Worked on embeddings, retrieval, or content-aware search at scale.
- Experience designing labeling taxonomies or running annotation programs.
- Experience training vision or multimodal models from scratch (not just calling APIs).
- ML/AI research background — papers, citations, or a research org on your resume.
- Hands-on time with big-data frameworks like Spark, Ray, or Daft.
- Worked on embeddings, retrieval, or content-aware search at scale.
- Experience designing labeling taxonomies or running annotation programs.
Skills & Technologies
AWSGo
About the Company
Eventual
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Job Details
Employment Type
Full Time
Location
San Francisco, US
Posted
3 months ago
Country
US