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 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 deploy, operate, debug, and optimize distributed, cloud-native database systems built in Rust and Python.

Your Role

• 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, cloud orchestration (Kubernetes, serverless-style deployments).

• Use and contribute to Python and Rust codebases: reproduce customer environments, inspect logs, build diagnostic tools, run instrumentation, apply patches and configuration changes.

What We’re Looking For
Must-have

• 8+ years of professional experience in a support / operations / troubleshooting role in a distributed database or data infrastructure environment.

• Demonstrated experience with one or more of the following: distributed database systems, cloud-native data platforms, vector/feature stores, analytics engines or big data systems.

• Proficiency in Rust and/or Python: you should be comfortable reading, navigating, and debugging code in these languages; ideally you’ve built or debugged production-quality systems in one or both.

• Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).

• 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.

• Experience with cloud platforms (AWS, GCP, or Azure) and Kubernetes or serverless deployment models for database workloads.

• Strong sense of ownership, urgency, correct prioritization under pressure, and ability to work closely with engineering teams to drive resolution.

Nice-to-have

• Prior experience supporting or operating large-scale open-source database deployments (e.g., vector search systems, NoSQL databases, distributed SQL, lakehouse/feature-store architectures).

• Familiarity with storage engine internals, indexing/data layout, performance tuning, and profiling tools.

• Contributions to open-source projects (especially Rust/Python), 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.

• Comfortable working in a fast-moving startup environment with high autonomy and evolving responsibilities.

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.