---
title: 'Senior Support Engineer at LanceDB'
canonical: 'https://feeny.ai/job/senior-support-engineer-lancedb-united-states-3gfjktwjnmv5'
type: 'job'
last_seen: '2026-09-06'
---

# Senior Support Engineer at LanceDB

- **Company:** LanceDB
- **Location:** United States
- **Compensation:** $180k–$250k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-03-17
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/lancedb/2fa13bf0-6f20-4ef0-a2d9-120018b7ee02/application

**Skills:** Distributed Database Systems, Cloud-Native Data Platforms, AWS, GCP, Azure, Kubernetes, Containerization, Autoscaling, Vector Stores, Feature Stores, Analytics Engines, Big Data Systems, Log Analysis, Shell Scripting, Python, Grafana, Vector DBs, Rust, Storage Engine Internals, Indexing

> The Senior Support Engineer serves as the primary technical point of contact for enterprise customers, bridging engineering and user needs. Responsibilities include debugging distributed Rust-based databases, maintaining support infrastructure, and collaborating with engineering teams to resolve complex issues and i...

## 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 hands-on, technically strong Support Engineer who will be the bridge between our engineering team and enterprise users of LanceDB, helping our customers debug distributed databases built in Rust.

## WHAT YOU’LL DO

- As one of the early team members, build our support infrastructure and practices while handling customer cases:
- Develop and maintain knowledge-base articles, runbooks, and support tooling that document common issues, best practices, deployment patterns, and performance tuning.
- Contribute to metrics around support response-times, resolution times, customer satisfaction, and help build a scalable support organization as we grow.
- Work proactively: identify recurring issues, escalate product bugs or UX gaps, propose improvements in the support process, and advocate for the customer in the roadmap.
- Serve as one of the primary technical points of contact for our customers: troubleshoot issues, respond to escalations, and guide customers through full lifecycle support for large-scale deployments of LanceDB.
- Work in close collaboration with our engineering and product teams to reproduce issues, debug root causes, propose remediation, and drive fixes or enhancements.
- Dive deeply into distributed database internals: query execution, storage engine, indexing, sharding, replication, fail-over, and cloud orchestration (Kubernetes, serverless-style deployments).
- Use and contribute to Rust codebases: reproduce customer environments, inspect logs, build diagnostic tools, run instrumentation, apply patches and configuration changes.

## WHAT WE’RE LOOKING FOR

Please do not apply unless you meet all of the criteria in this section.

- 8+ years of professional experience in a support / operations / troubleshooting role in a distributed database or data infrastructure environment.
- Demonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models.
- Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).
- Demonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems.
- Very comfortable with reading logs and correlating them with source code, working with Grafana dashboards, and creating shell scripts or Python code to assist in debugging.
- Excellent customer-facing communication skills: you'll be working directly with high-value customers, so you must be comfortable explaining complex technical issues clearly, managing expectations, and advocating for the customer.
- Strong sense of ownership, urgency, correct prioritization under pressure, and ability to work closely with engineering teams to drive resolution.
- Comfortable working in a fast-moving startup environment with high autonomy and evolving responsibilities.

## NICE TO HAVE

- Proficiency in Rust: you should be comfortable reading, navigating, and debugging code; ideally you've built or debugged production-quality systems written in Rust.
- Familiarity with storage engine internals, indexing/data layout, performance tuning, and profiling tools.
- Contributions to open-source projects (especially Rust), or experience writing diagnostic tools, debuggers, or instrumentation.
- Experience deploying and monitoring systems in large-scale production environments: logging/observability (e.g., Prometheus, Grafana, OpenTelemetry), alerting, SLOs/SLAs.
- Previous experience creating new runbooks, selecting and configuring support ticketing systems, and defining incident response processes.

## 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
- [Founding Recruiter](https://feeny.ai/job/founding-recruiter-lancedb-united-states-canada-pys9nmbxc9r6) — United States / Canada
- [Senior Customer Success Engineer](https://feeny.ai/job/senior-customer-success-engineer-lancedb-united-states-canada-2vht5p4k14na) — United States / Canada
- [Senior Solutions Engineer](https://feeny.ai/job/senior-solutions-engineer-lancedb-united-states-canada-ssezdjgy9qsx) — United States / Canada
- [Partner Solutions Architect](https://feeny.ai/job/partner-solutions-architect-lancedb-united-states-canada-yzqwrh2zb7kk) — United States / Canada
- [Senior UX/AX Engineer](https://feeny.ai/job/senior-ux-ax-engineer-lancedb-united-states-canada-0hzxgz55kxjj) — United States / Canada
- [Senior Solutions Engineer](https://feeny.ai/job/senior-solutions-engineer-lancedb-san-francisco-0btn7w423bah) — San Francisco, CA