---
title: 'Senior System Test Engineer at LanceDB'
canonical: 'https://feeny.ai/job/senior-system-test-engineer-lancedb-united-states-canada-as5kbb9zexxh'
type: 'job'
last_seen: '2026-10-18'
---

# Senior System Test Engineer at LanceDB

- **Company:** LanceDB
- **Location:** United States / Canada
- **Compensation:** $180k–$250k
- **Employment:** full-time
- **Posted:** 2026-10-13
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/lancedb/0ecdfb7f-a6cb-4ee2-b4c0-a3163539a265

## Job description

## 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

As 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 Do

- System 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.

## What We're Looking For

- 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

## Nice to Have

- 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

## About LanceDB

## Core Business
- **Primary industry**: AI data infrastructure / vector databases / multimodal lakehouse
- **Target customers**: B2B – AI/ML teams, enterprises building generative AI, recommendation systems, and large-scale training pipelines.
- **Mission/purpose statement**: “Build Better Models, Faster” by providing a unified foundation to accelerate training dataset development.

## Products & Services
- **Embedded Vector Database**: Open-source vector database for multimodal AI, scaling to billions of embeddings with minimal management. Supports vector/semantic, full-text, and hybrid search combined with SQL filters.
- **Multimodal Lakehouse (LanceDB Enterprise)**: A unified platform for curation, feature engineering, retrieval, and training at massive scale. Includes automated pre-processing, versioning, branching, and rollback.
- **LanceDB Cloud**: Managed cloud service with auto re-indexing, caching, and a management dashboard – no server management required.
- **Lance Format**: A modern columnar data format optimized for AI training, analytics, and retrieval – significantly faster than Parquet and WebDataset.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total funding $41.1M; annual revenue $1.2M (LinkedIn estimate)
- **Notable Investors/Partners**: Theory Ventures (led Series A), CRV, Swift Ventures, Y Combinator.
- **Growth Signals**:
  - Headcount: 32 employees (+135% YoY, +9.3% monthly growth)
  - Customers include Midjourney, Runway (testimonials on lancedb.com)
  - 11,382 LinkedIn followers (+60.1% yearly)
  - 6 active job postings; monthly job posting trend +20%

## Competitive Advantages
- **Open-source core** with a permissive license, enabling community adoption and vendor lock-in avoidance.
- **Lance columnar format** – purpose-built for AI workloads, offering fast random access, column appends without rewriting, and 70% Model FLOPS Utilization (MFU) during training.
- **Multimodal by design** – stores vectors, images, tensors, video, and metadata in a single table, replacing 3–4 different data stores.
- **SSD-based ANN index** – scales beyond memory for low-latency billion-scale vector search on a single node.

## Strategic Focus
- Accelerate development of the Multimodal Lakehouse (announced with Series A)
- Expand enterprise features (managed cloud, security, compliance)
- Grow ecosystem integrations (Spark, Python UDFs, etc.)
- Scale go-to-market and customer success (hiring Forward Deployed Engineers, Solutions Engineers)

## Why Work Here
- **Culture**: Startup environment with deep open-source roots; founders are former pandas co-author (Chang She) and HDFS/core contributor (Lei Xu). Engineering-heavy team (38% technical).
- **Remote/hybrid policy**: Not explicitly stated; likely flexible given distributed team (US, Canada, China) and startup nature.
- **Notable perks/engineering culture**: Work on foundational AI infrastructure used by Midjourney and Runway. Opportunities to contribute to open-source, build in Rust, and shape the multimodal data ecosystem. Fast-growing company with strong investor backing.

## Sources
1. [lancedb.com](https://www.lancedb.com/)
2. [crunchbase.com](https://www.crunchbase.com/organization/lancedb)
3. [linkedin.com](https://www.linkedin.com/company/lancedb)
4. [ycombinator.com](https://www.ycombinator.com/companies/lancedb)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/lancedb)

## Other roles at LanceDB

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- [Senior Solutions Engineer](https://feeny.ai/job/senior-solutions-engineer-lancedb-san-francisco-0btn7w423bah) — San Francisco, CA
- [AI Research Engineer](https://feeny.ai/job/ai-research-engineer-lancedb-united-states-kzr7s7kn5xsw) — United States
- [Senior Product Manager](https://feeny.ai/job/senior-product-manager-lancedb-united-states-canada-kddh90xzsr7s) — United States / Canada
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- [Senior Customer Success Engineer](https://feeny.ai/job/senior-customer-success-engineer-lancedb-united-states-canada-2vht5p4k14na) — United States / Canada