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LanceDB is an AI-native Multimodal Lakehouse that enables researchers to curate and manage petabytes of video, audio, and derived signals with minimal code, accelerating AI training workflows. The company serves leading AI labs including Runway, Midjourney, and Netflix.
As an AI Research Engineer, you will work closely with the research team on open-ended research projects focused on end-to-end training flows across different AI domains. Your primary mission is to demonstrate how LanceDB accelerates research workflows and to drive product awareness through rigorous experimentation and thought leadership.
Key responsibilities include:
- Showcase LanceDB's capabilities for end-to-end model training (from data curation to modeling) across industry verticals
- Design and execute well-controlled, replicable experiments comparing LanceDB workflows against standard training approaches, including benchmarking studies
- Create core content and collaborate cross-functionally to highlight workflow-specific features such as blobv2 and distributed indexing
- Publish research papers and models on the LanceDB blog, social media, and at AI conferences
- Partner with engineering and product teams to provide researcher-focused feedback that informs product development
This role blends research autonomy with engineering execution, offering the opportunity to pursue your research interests while meaningfully impacting product adoption and awareness.
REQUIREMENTS:
- 5+ years of experience training deep learning models (not limited to LLMs); prior work with video, action, or world models is ideal
- Proven track record of training state-of-the-art models in an industry vertical
- Strong experience building and maintaining popular open-source repositories
- Demonstrated ability to synthesize user feedback and translate it into key product deliverables
- Excellent prioritization and execution efficiency
- Strong product GTM sense; ability to balance strategic thinking with hands-on execution
- Passion for staying current with state-of-the-art AI research and trends
NICE TO HAVE:
- Experience with transformer-based model training and post-training/alignment
- Hands-on experience with PyTorch, distributed training, and tensor parallelism
- 5+ years of experience including startup environments
