--- title: 'AI Research Engineer at LanceDB' canonical: 'https://feeny.ai/job/ai-research-engineer-lancedb-united-states-kzr7s7kn5xsw' type: 'job' last_seen: '2026-10-18' --- # AI Research Engineer at LanceDB - **Company:** LanceDB - **Location:** United States - **Compensation:** $150k–$255k - **Employment:** full-time - **Posted:** 2026-09-14 - **Last confirmed live:** 2026-10-18 - **Apply:** https://jobs.ashbyhq.com/lancedb/d72f807c-7d73-4356-8f49-4597293aa5ef ## Job description ## 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 ## About LanceDB ## Core Business - **Primary industry**: AI data infrastructure / vector databases / multimodal lakehouse - **Target customers**: B2B – AI/ML teams, enterprises building generative AI, recommendation systems, and large-scale training pipelines. - **Mission/purpose statement**: “Build Better Models, Faster” by providing a unified foundation to accelerate training dataset development. ## Products & Services - **Embedded Vector Database**: Open-source vector database for multimodal AI, scaling to billions of embeddings with minimal management. Supports vector/semantic, full-text, and hybrid search combined with SQL filters. - **Multimodal Lakehouse (LanceDB Enterprise)**: A unified platform for curation, feature engineering, retrieval, and training at massive scale. Includes automated pre-processing, versioning, branching, and rollback. - **LanceDB Cloud**: Managed cloud service with auto re-indexing, caching, and a management dashboard – no server management required. - **Lance Format**: A modern columnar data format optimized for AI training, analytics, and retrieval – significantly faster than Parquet and WebDataset. ## Market Standing - **Valuation/Market Cap**: Not disclosed - **Key Metric**: Total funding $41.1M; annual revenue $1.2M (LinkedIn estimate) - **Notable Investors/Partners**: Theory Ventures (led Series A), CRV, Swift Ventures, Y Combinator. - **Growth Signals**: - Headcount: 32 employees (+135% YoY, +9.3% monthly growth) - Customers include Midjourney, Runway (testimonials on lancedb.com) - 11,382 LinkedIn followers (+60.1% yearly) - 6 active job postings; monthly job posting trend +20% ## Competitive Advantages - **Open-source core** with a permissive license, enabling community adoption and vendor lock-in avoidance. - **Lance columnar format** – purpose-built for AI workloads, offering fast random access, column appends without rewriting, and 70% Model FLOPS Utilization (MFU) during training. - **Multimodal by design** – stores vectors, images, tensors, video, and metadata in a single table, replacing 3–4 different data stores. - **SSD-based ANN index** – scales beyond memory for low-latency billion-scale vector search on a single node. ## Strategic Focus - Accelerate development of the Multimodal Lakehouse (announced with Series A) - Expand enterprise features (managed cloud, security, compliance) - Grow ecosystem integrations (Spark, Python UDFs, etc.) - Scale go-to-market and customer success (hiring Forward Deployed Engineers, Solutions Engineers) ## Why Work Here - **Culture**: Startup environment with deep open-source roots; founders are former pandas co-author (Chang She) and HDFS/core contributor (Lei Xu). Engineering-heavy team (38% technical). - **Remote/hybrid policy**: Not explicitly stated; likely flexible given distributed team (US, Canada, China) and startup nature. - **Notable perks/engineering culture**: Work on foundational AI infrastructure used by Midjourney and Runway. Opportunities to contribute to open-source, build in Rust, and shape the multimodal data ecosystem. Fast-growing company with strong investor backing. ## Sources 1. [lancedb.com](https://www.lancedb.com/) 2. [crunchbase.com](https://www.crunchbase.com/organization/lancedb) 3. [linkedin.com](https://www.linkedin.com/company/lancedb) 4. [ycombinator.com](https://www.ycombinator.com/companies/lancedb) 5. 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