Asks for

The ad says“Demonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems.”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“Demonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems.”
The ad says“Demonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models.”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“Demonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models.”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“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.”
The ad says“Demonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models.”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“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.”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).”
The ad says“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.”
The ad says“Demonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems.”

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

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.

What You’ll Do

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

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 response processes.