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.