---
title: 'Senior Solutions Engineer at LanceDB'
canonical: 'https://feeny.ai/job/senior-solutions-engineer-lancedb-united-states-canada-ssezdjgy9qsx'
type: 'job'
last_seen: '2026-09-06'
---

# Senior Solutions Engineer at LanceDB

- **Company:** LanceDB
- **Location:** United States / Canada
- **Compensation:** $180k–$250k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-07-10
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/lancedb/90a7a3d6-4b9f-4501-92b5-7cef77c4f1c9

## Job description

## ABOUT LANCEDB

LanceDB http://lancedb.com/ is a developer-friendly, open-source data lake for multimodal AI. From hyper-scalable vector search to advanced retrieval for RAG, from streaming training data to interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application, and powers some of the most groundbreaking applications and challenging requirements today.

## ABOUT THE ROLE

We are looking for a Senior Solutions Engineer who blends deep technical understanding of AI/ML infrastructure with excellent communication and solution-building skills. In this role, you will serve as a trusted advisor to prospective customers, design partners, and strategic accounts—bridging the gap between cutting-edge AI engineering and real-world business use cases.

This role is ideal for someone who thrives at the intersection of technical depth and customer interaction, and who enjoys crafting solutions, demos, and integrations that showcase LanceDB’s strengths in production environments.

## YOUR RESPONSIBILITIES WILL INCLUDE

- Serve as the technical lead in pre-sales conversations—partnering with account executives to scope, solution, and articulate the value of LanceDB for customer-specific workflows.
- Lead technical discovery and architecture design sessions with prospects across verticals including AI infra, LLM ops, and multimodal data pipelines.
- Build and deliver custom demos and proof-of-concepts to highlight how LanceDB solves challenging RAG, vector search, and feature engineering problems.
- Act as the bridge between customer pain points and our engineering/product teams—informing roadmap priorities with real-world feedback.
- Partner closely with design partners and early adopters to ensure successful onboarding and expansion.
- Champion a superior developer experience with a sharp focus on documentation, SDK ergonomics, and integration workflows.

## REQUIREMENTS

- You thrive in a fast-paced, startup environment and enjoy working with high-caliber teams.
- You have 5+ years of experience in a Sales Engineer, Solutions Engineer, ML Engineer, or AI Infrastructure role, supporting AI/ML products or platforms.
- Strong knowledge of AI/ML frameworks like PyTorch or TensorFlow, and how they integrate with infrastructure for model training, fine-tuning, and inference.
- Hands-on experience working with distributed systems such as Ray, Spark, or Kubernetes.
- Familiarity with cloud services (AWS, GCP, Azure) including compute and storage (e.g., EC2, GKE, S3).
- Confident communicating with both technical and non-technical stakeholders, and able to translate complex infrastructure into actionable solutions.

## BONUS POINTS IF YOU

- Have experience building or supporting feature engineering workflows or vector search pipelines.
- Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines.
- Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK).
- Are familiar with open-source data/streaming frameworks such as Apache Spark, Flink, Delta Lake, Kafka, or Airflow.
- Have deep Python skills or are curious about Rust.
- Are comfortable creating technical content, workshops, or presenting at meetups/conferences.
- Have experience deploying ML infrastructure in customer environments using tools like Terraform, Docker, and CI/CD pipelines.
- Have supported enterprise customers or worked in a customer-facing technical capacity before.

## ABOUT THE LANCEDB TEAM

LanceDB was created by experts with decades of experience building tools for data science and machine learning. From co-authors of pandas to Apache PMC of HDFS, Arrow, Iceberg and HBase, the LanceDB team has created open-source tools used by millions worldwide.

## 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)

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