AI Research Engineer

About LanceDB

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 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

• 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

• Passion for staying up-to-date with SOTA AI research and trends

• 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