Senior Customer Success Engineer

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LanceDB

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

LanceDB is the first open-source AI-Native Multimodal Lakehouse designed to handle complex AI data needs on a large scale. It offers a unified solution for multimodal data, integrating storage, search, feature engineering, and analytics into a single platform. This enables users to efficiently manage diverse data types and workflows, facilitating rapid iteration and deployment of AI models. With advanced capabilities such as hybrid search and optimized training pipelines, LanceDB is built for enterprises seeking to leverage their data effectively in AI applications.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• Offers Equity