Senior Support Engineer

About LanceDB LanceDB is a high-performance, open-source, cloud-native database built for multimodal workflows. From vector search at multi-billion scale to real-time retrieval, feature engineering, and analytics across large-scale datasets, LanceDB powers AI 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 debug distributed databases built in Rust. 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, and cloud orchestration (Kubernetes, serverless-style deployments). - Use and contribute to Rust codebases: reproduce customer environments, inspect logs, build diagnostic tools, run instrumentation, apply patches and configuration changes. WHAT WE’RE LOOKING FOR MUST-HAVE (PLEASE DO NOT APPLY UNLESS YOU MEET ALL OF THE CRITERIA IN THIS SECTION) - 8+ years of professional experience in a support / operations / troubleshooting role in a distributed database or data infrastructure environment. - Demonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models. - Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling). - Demonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems. - Very comfortable with reading logs and correlating them with source code, working with Grafana dashboards, and creating shell scripts or Python code to assist in debugging. - 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. - Strong sense of ownership, urgency, correct prioritization under pressure, and ability to work closely with engineering teams to drive resolution. - Comfortable working in a fast-moving startup environment with high autonomy and evolving responsibilities. NICE-TO-HAVE - Proficiency in Rust: you should be comfortable reading, navigating, and debugging code; ideally you’ve built or debugged production-quality systems written in Rust. - Familiarity with storage engine internals, indexing/data layout, performance tuning, and profiling tools. - Contributions to open-source projects (especially Rust), 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. - Previous experience creating new runbooks, selecting and configuring support ticketing systems, and defining incident r