AI Research Engineer

Theory; United States; In loco; USD 150.000 - 230.000; Tempo pieno

Descrizione del lavoro

LanceDB is seeking an AI research engineer to join the research team and drive end-to-end training workflows across diverse AI domains. You will help accelerate research flows by demonstrating how LanceDB can streamline data curation, modeling, and evaluation across industry verticals.

You will publish models and research work, collaborate closely with engineering and product, and contribute to building popular OSS projects while staying at the forefront of SOTA AI research and trends.

Competenze

• 5+ years of experience in training deep learning models, including video, action, or world models.
• 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 execution efficiency.
• Strong sense of product GTM and ability to balance strategy with hands-on work.
• Passion for staying up-to-date with SOTA AI research and trends.

Mansioni

• Show how LanceDB can be used for end-to-end model training from data curation to modeling across verticals.
• Benchmark and compare Lancedb workflows with standard training pipelines and design replicable experiments.
• Collaborate cross-functionally with engineering and product to improve workflows and features.
• Publish models and research papers on LanceDB platforms and at AI conferences.
• Provide researcher perspective by giving product feedback and influencing roadmap.

Conoscenze

Deep learning model training

SOTA models

OSS maintenance

User feedback mapping

Prioritization

Product GTM

SOTA trends

Strumenti

PyTorch

Tensor parallelism

Distributed training

Descrizione del lavoro

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.

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 You'll Do

• 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

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

• 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

Nice to Have

• 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