--- title: 'Senior Software Engineer at LanceDB' canonical: 'https://feeny.ai/job/senior-software-engineer-lancedb-united-states-yw7xbmcym166' type: 'job' last_seen: '2026-09-06' --- # Senior Software Engineer at LanceDB - **Company:** LanceDB - **Location:** United States - **Compensation:** $180k–$250k - **Employment:** full-time - **Work type:** remote - **Posted:** 2025-10-25 - **Last confirmed live:** 2026-09-06 - **Apply:** https://jobs.ashbyhq.com/lancedb/9454302d-3d40-4ed4-832e-c1590ff497fc/application **Skills:** High-performance databases, Big data systems, Spark, Hive Metastore, Presto, Trino, Ray, Java, Scala, Rust, Distributed systems, Apache, C++, Apache Arrow, DataFusion, Parquet, Iceberg, Delta Lake, Flink, ClickHouse > The Senior Open Source Engineer will expand LanceDB's reach by integrating the Lance format with major data infrastructure systems like Spark and Trino. Responsibilities include designing distributed dataset operations, building efficient indices, and improving internal data processing infrastructure using Rust and... ## 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 We're looking for a Senior Software Engineer to help expand the reach of Lance and LanceDB within the broader data infrastructure ecosystem. You'll work at the intersection of high-performance computing, big data, and open-source systems. You will contribute scale and performance improvements, integrations with the wider data and AI ecosystem, simplifying distributed operations, and usability and maintainability enhancements. ## What You'll Do - Designing and maintaining efficient distributed Lance dataset operations - Building efficient indices to enable predicate pushdown and accelerate queries in Spark, Ray, or Trino - Working on table formats, data encodings, and various aspects of the Lance format in Rust - Driving open-source community efforts to integrate the Lance format with Spark, Hive Metastore, Presto, Trino, Ray, and other data infrastructure systems - Operating and improving internal data processing infrastructure - Promoting the Lance format in open-source communities and at Big Data conferences ## What We're Looking For - 10+ years of experience building high-performance databases, big data systems, or large-scale data services - Deep understanding of internals of open-source Big Data or AI training systems (e.g., Hadoop, Spark, Flink, Ray, Iceberg, Delta Lake, Hudi, ClickHouse, Trino, Presto, PyTorch, or JAX) - Strong experience with high-performance computing in C++, Java, and/or Scala - Experience with Rust (or willingness to learn it) - Proven ability to move fast, work independently, and collaborate with a high-caliber team ## Nice to Have - Contributor, committer, or PMC member in Apache or other large open-source projects - Experience with Apache Arrow, DataFusion, Parquet, Iceberg, or Delta Lake - Track record of driving large features or integrations in distributed systems - Strong community presence and passion for open-source collaboration ## 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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