LanceDB is a developer-friendly, open source database for multimodal AI. From hyper scalable vector search to advanced retrieval for RAG, from streaming training data to interactive exploration of…
LanceDB is a developer-friendly, open source database for multimodal AI. From hyper scalable vector search to advanced retrieval for RAG, from streaming training data to interactive exploration of…
What they do: Open-source multimodal lakehouse and vector database for large-scale AI workloads
Founded / HQ: Launched 2022; headquartered in San Francisco
Founders: Chang She (CEO) and Lei Xu (CTO)
Funding: Series A $30M (Jun 24, 2025); total funding reported ~$41M
Team size: ~24 employees
Company Overview
Problem Domain
Data infrastructure for multimodal and generative AI applications (vector search, retrieval-augmented generation, model training pipelines).
Founded
2022
Industry
Databases / AI infrastructure
Tech Stack
Vector database
Multimodal lakehouse
Hybrid search
SQL
UDFs
Compute-storage separation
Funding Track Record
Pre-seed
Seed- 2024-05
Seed round mentioned as led by CRV (May 2024 / June 2024 announcement)
Series A- 2025-06-24
30000000.00
Series A announced June 24, 2025 with participation from CRV, Databricks Ventures, Y Combinator, Runway, Zero Prime, and Swift Ventures
Investor Signal
“Investors include Theory Ventures (lead, Series A), CRV, Databricks Ventures, Y Combinator, Runway, Zero Prime, and Swift Ventures”
Founders
What we do
Join the Team
Senior Customer Success Engineer
On-SiteSan Francisco, CA, US
On-Site • San Francisco, CA, US
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About LanceDB
LanceDB is a high-performance, open-source, cloud-native database built for AI-native and multimodal workflows. From vector search at multi-billion-scale to real-time retrieval, feature engineering, and analytics across large-scale datasets, LanceDB powers cutting-edge applications of machine learning and data infrastructure.
We’re building the next generation of intelligent, data-driven systems — and we’re looking for an experienced, customer-focused engineer who can help our enterprise users deploy, operate, and scale LanceDB successfully.
Your 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.
In This Role, You Will
Partner closely with customers to design, deploy, and optimize LanceDB in production environments, ensuring reliability, scalability, and performance for distributed, cloud-native workloads.
Must-haveWhat 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).
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.
Why Join Us
You’ll join a world-class team of open-source builders (co-authors of pandas, and contributors to HDFS, Arrow, Iceberg, and HBase) working on cutting-edge AI infrastructure. You’ll collaborate on systems that power next-generation AI workloads while shaping how LanceDB operates and scales production environments.
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Lead technical onboarding and architecture reviews; provide best-practice guidance on system configuration, query optimization, and integration patterns in Rust and Python.
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.
As the first senior hire in this function, shape processes, tooling, and team culture as we scale customer success and post-sales engineering.
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.