--- title: 'Senior UX/AX Engineer at LanceDB' canonical: 'https://feeny.ai/job/senior-ux-ax-engineer-lancedb-united-states-canada-0hzxgz55kxjj' type: 'job' last_seen: '2026-09-13' --- # Senior UX/AX Engineer at LanceDB - **Company:** LanceDB - **Location:** United States / Canada - **Compensation:** $180k–$250k - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-06-26 - **Last confirmed live:** 2026-09-13 - **Apply:** https://jobs.ashbyhq.com/lancedb/12ef025d-0136-4197-b50c-89a520dc493b/application **Skills:** TypeScript, React, Next.js, Vercel, HTML, CSS, Frontend Development, Backendpoint-to-backend development, Open Source Contribution, Kubernetes, AWS Lambda, Terraform, CI/CD, DevOps, Interaction Design > The Senior Fullstack Engineer will join the LanceDB team to polish the user experience for high-performance vector databases in the cloud. Responsibilities include building scalable backends for LanceDB Cloud, optimizing user experience through collaboration with designers, and defining next-generation data systems... ## 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 - Engineer focusing on providing optimal user and agent experiences on the LanceDB platform. - Takes high-level direction (e.g., “identify where UX is significant ahead of AX and drive more parity”) and drives to results - Success looks like a LanceDB platform that incorporates compelling user and agent experiences for exploring and managing their multimodal data, enabling more adoption and increased usage. ## What You’ll Do - Work with the rest of engineering to optimize user and agent experience across their parts of the platform. - Build scalable backends for LanceDB Enterprise to support user and agent experiences across billion-scale datasets. - Our tech stack: Rust, Typescript, React, Python, k8s, Terraform, CSPs ## What We’re Looking For - 8+ years as a software engineer - Experience working on frontend and backend parts of large-scale data platforms (e.g., databases, data infra, ML/AI systems), including work on concurrency and multitenancy - Experience crafting UIs using Typescript, React/next.js/Vercel, CLIs, and APIs - Experience building elegant user-oriented and agent-oriented experiences - Experience building infrastructure with CSP stacks, Terraform, and k8s - Demonstrated ownership of ambiguous projects with minimal guidance - Strong written and verbal communication (can drive alignment across teams) - Comfortable using data (metrics, experiments, usage) to guide decisions - Experience contributing to or working with open source communities ## 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. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/lancedb) ## Other roles at LanceDB - [Senior Product Manager](https://feeny.ai/job/senior-product-manager-lancedb-united-states-canada-kddh90xzsr7s) — United States / Canada - [Controller](https://feeny.ai/job/controller-lancedb-united-states-6rafknnjaa3f) — United States - [Senior Product Security Engineer](https://feeny.ai/job/senior-product-security-engineer-lancedb-united-states-canada-0b3xt2p24427) — United States / Canada - [Founding Revenue Marketer](https://feeny.ai/job/founding-revenue-marketer-lancedb-united-states-canada-mcb1thtdv07v) — United States / Canada - [Senior Customer Success Engineer](https://feeny.ai/job/senior-customer-success-engineer-lancedb-united-states-canada-2vht5p4k14na) — United States / Canada - [Partner Solutions Architect](https://feeny.ai/job/partner-solutions-architect-lancedb-united-states-canada-yzqwrh2zb7kk) — United States / Canada - [Senior Support Engineer](https://feeny.ai/job/senior-support-engineer-lancedb-united-states-3gfjktwjnmv5) — United States - [Senior Solutions Engineer](https://feeny.ai/job/senior-solutions-engineer-lancedb-san-francisco-0btn7w423bah) — San Francisco, CA - [Senior Software Engineer](https://feeny.ai/job/senior-software-engineer-lancedb-united-states-yw7xbmcym166) — United States