Startups · AI

AI Research Engineer

LanceDB · United States · On-site

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About LanceDB

The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster. Backed by Y Combinator and CRV.

About the role

As the AI research engineer at LanceDB, you'll work with the research team to perform fairly open ended research, focused on end to end training flows across different AI domains, showcasing how LanceDB can be used to accelerate research flows. This is an opportunity to pursue your research interest as an engineer, and have a meaningful impact on raising awareness and significantly improve the product.

What they're looking for

  • 5+ years of experience in training deep learning models, not limited to LLM, ideally have worked with video, action, world models before
  • Proven track record of training SOTA models in an industry vertical
  • Strong experience in building and maintaining popular OSS repos
  • Demonstrated ability to map user feedback from noise to key deliverables
  • Excellent prioritization skills and demonstrate execution efficiency
  • Strong sense of product GTM, demonstrate ability to balance strategic thinking with hands-on execution
More about this role

AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.

As the AI research engineer at LanceDB, you'll work with the research team to perform fairly open ended research, focused on end to end training flows across different AI domains, showcasing how LanceDB can be used to accelerate research flows.

This is an opportunity to pursue your research interest as an engineer, and have a meaningful impact on raising awareness and significantly improve the product.

Show how Lancedb can be used for training models end to end from curation to modeling across industry verticals

Compare the Lancedb stacked workflow with existing standard training flows with well designed and replicable experiments, that may include benchmarking

Provide core content and work cross-function with to...

Read the full posting on LanceDB's site ↗

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