Senior Customer Success Engineer

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.