---
title: 'Senior Customer Success Engineer at LanceDB'
canonical: 'https://feeny.ai/job/senior-customer-success-engineer-lancedb-united-states-canada-2vht5p4k14na'
type: 'job'
last_seen: '2026-09-06'
---

# Senior Customer Success Engineer at LanceDB

- **Company:** LanceDB
- **Location:** United States / Canada
- **Compensation:** $180k–$250k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-07-10
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/lancedb/52bee912-0a16-4815-bc3d-9bf14cf6e372

## 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 Senior Customer Success Engineer, you will be a trusted advisor to our most strategic customers. You'll combine deep technical expertise with outstanding communication and relationship-building skills to ensure customers achieve success in deploying and scaling LanceDB across production workloads.

You'll guide customers from onboarding through adoption and expansion — while flexing seamlessly into pre-sales or technical support capacities as business needs require. You'll serve as the connective tissue between our customers, product, and engineering teams, driving continuous improvement in both the customer experience and the product itself.

## WHAT YOU’LL DO

- Partner closely with customers to design, deploy, and optimize LanceDB in production environments, ensuring reliability, scalability, and performance for distributed, cloud-native workloads.
- Lead technical onboarding and architecture reviews; provide best-practice guidance on system configuration, query optimization, and integration patterns.
- Proactively identify adoption barriers, troubleshoot complex distributed-system issues, and coordinate with product and engineering teams to drive timely resolutions.
- Own customer success metrics: deployment time, usage growth, retention, and satisfaction. Build dashboards and track health across accounts.
- Develop and deliver technical enablement: create sample code, automation tools, and documentation to accelerate customer outcomes.
- Serve as the customer's technical advocate internally — communicating feature requests, influencing roadmap priorities, and improving developer experience.
- Collaborate cross-functionally with sales engineering (for technical evaluations, proofs-of-concept, and demos) and support engineering (for escalations and issue triage).
- Contribute to internal tooling, runbooks, and playbooks that will form the foundation of LanceDB's future customer success organization.
- Help to shape processes, tooling, and team culture as we scale customer success and post-sales engineering.

## WHAT WE’RE LOOKING FOR

- 10+ years of professional experience in technical roles such as post-sales engineering, customer success, solutions architecture, or technical support, ideally within the data infrastructure or distributed systems space.
- Proven track record supporting or deploying distributed database systems or large-scale cloud-native data platforms (e.g., high-availability, multi-region, and horizontally scalable environments).
- Strong proficiency in Rust and Python — able to read, debug, and write production-grade code in both languages.
- Deep understanding of distributed systems concepts: sharding, replication, consensus, partitioning, failure recovery, and performance tuning.
- Experience deploying and managing workloads on Kubernetes or other container orchestration frameworks, and familiarity with cloud environments (AWS, GCP, Azure).
- Exceptional communication and presentation skills: able to engage directly with customers' engineering leaders, architects, and executives with credibility and empathy.
- Strong problem-solving ability, coupled with a customer-first mindset and the ability to operate autonomously in fast-moving, ambiguous environments.
- Willingness and ability to flex across functions — including pre-sales engineering, technical support, and post-sales enablement — as needed by the business.

## NICE TO HAVE

- Previous experience as a founding or early member of a customer success or solutions engineering function at a high-growth startup.
- Hands-on experience with vector search, feature stores, or AI-native data systems.
- Contributions to open-source projects (especially in Rust or Python) or experience authoring developer-facing technical content.
- Familiarity with modern observability stacks (Prometheus, Grafana, OpenTelemetry) and incident management best practices.
- Experience designing or leading enterprise architecture workshops or technical proof-of-concepts.

## 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
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- [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 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 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