AI Research Engineer

Responsibilities

• 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 increase awareness for workflow specific features like blobv2, distributed indexing etc.
• Publish models and research papers on LanceDB blog platform, social media, and in AI conferences
• Partner closely with engineering and product to provide feedback from a researcher’s perspective

Requirements

Excellent prioritization skills and demonstrate execution efficiency

• Strong sense of product GTM, demonstrate ability to balance strategic thinking with hands-on execution
• Passion for staying up-to-date with SOTA AI research and trends

Preferred

• Experience with training transformer based models, and post-training/alignment
• Hands on experience with PyTorch, distributed training, and tensor parallelism
• 5+ years of experience, including working at startups

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 We'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