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 RoleAs the Senior System Test Engineer at LanceDB, you will drive the correctness, reliability, and performance of Lance and LanceDB under demanding production conditions. You'll work at the intersection of distributed databases, systems engineering, and test infrastructure, building the frameworks and workloads that expose failures before our users encounter them.
You'll partner closely with engineering to turn architectural risks and real customer workloads into automated, reproducible tests, and to diagnose and fix complex failures. This is a hands-on role with ownership of system-level test strategy and execution, where you'll use AI tooling adeptly to accelerate development and investigation while maintaining rigorous validation.
This is an opportunity to define the quality bar for infrastructure powering the next generation of AI workloads, and to directly influence product architecture, release confidence, and the reliability our users depend on as they move from experimentation to production at scale.
What You'll DoSystem test strategy: Define and drive system-level testing across correctness, reliability, scalability, and performance, prioritizing the highest-risk behaviors in the platform.
Test frameworks and workloads: Build automated test frameworks and realistic workloads covering ingestion, concurrent reads and writes, indexing, querying, and dataset maintenance.
Data integrity and fault injection: Validate data integrity and documented consistency guarantees under concurrency, retries, partial failures, and recovery. Develop fault-injection and resilience tests for process crashes, network disruptions, storage errors, resource exhaustion, and interrupted operations.
Performance testing: Design load, stress, and long-running soak tests, and identify regressions in latency, throughput, resource usage, and search quality.
Upgrades and compatibility: Test upgrades, compatibility, and recovery scenarios to ensure the platform behaves predictably as software and workloads evolve.
Partner closely with engineers to investigate failures using logs, metrics, traces, and source code, reduce complex issues to minimal reproductions, and drive fixes and regression coverage.
Develop and continuously improve reproducible test environments, integrating reliable, actionable checks into CI and release workflows.
Use AI coding assistants and other AI tools to develop test harnesses, explore failure scenarios, analyze results, and accelerate debugging, independently verifying generated code and conclusions.
10+ years of experience in software engineering, system test engineering, or test infrastructure, with substantial experience building or validating distributed databases or large-scale data systems
Deep understanding of distributed databases, including consistency models, replication, partitioning, concurrency control, durability, and failure recovery
Proven track record of designing tests that verify system invariants and uncover data corruption, race conditions, and failures that emerge only under load or partial outages
Strong programming skills in Python and at least one systems language such as Rust, C++, Java, or Go, with the ability to build maintainable test infrastructure and debug product code
Hands-on experience with fault injection, performance testing, workload generation, and automated testing in distributed environments
Demonstrated ability to use AI tooling adeptly in daily engineering work, including coding assistants and agent-based workflows, with sound judgment about validating outputs
Experience with Linux, cloud infrastructure, containers, CI/CD, and observability tools
Ability to own ambiguous projects, prioritize risk, communicate findings clearly, and collaborate closely with engineers to drive issues to resolution
Experience testing database storage engines, query engines, vector search, or cloud object storage integrations
Familiarity with LanceDB, Lance, Apache Arrow, DataFusion, Parquet, or related data infrastructure
Experience with property-based testing, fuzzing, differential testing, or distributed consistency testing
Experience with Kubernetes, infrastructure as code, and large-scale test environment orchestration
Contributions to open-source databases, distributed systems, or testing frameworks
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