Forward Deployed Engineer

Join to apply for the Forward Deployed Engineer role at LanceDB

Location:

San Francisco Bay Area (In-Person / Hybrid)

Team:
Field Engineering

Job Type: Full-Time

Lance

DB is an open‑source, cloud‑native vector database and multimodal AI lakehouse built on a high‑performance columnar format. It enables developers and enterprises to build scalable, real‑time search and analytics applications across vectors, structured data, and AI workflows. Lance

DB delivers both embedded and managed deployment models with rich SDKs in Rust, Python, and other languages, and is purpose‑built to power state‑of‑the‑art retrieval, feature engineering, and large‑scale AI systems.

As a Forward Deployed Engineer (FDE) at Lance

DB, part of the Field Engineering organization, you will operate at the intersection of deep systems engineering and direct customer engagement. You will work hands‑on with strategic customers in the Bay Area to design, deploy, and scale Lance

DB in demanding production environments. This is a highly technical, product‑facing role. You will not only deliver customer solutions, but also contribute production‑quality code and actionable feedback directly back to Lance

DB’s core product lines.

• Lead on‑site and remote technical deployments of Lance
  
  DB with enterprise and strategic customers, including architecture design, benchmarking, performance tuning, and operational hardening.
• Write and maintain production‑grade code in Rust and Python for customer integrations, SDK enhancements, ingestion pipelines, and internal tooling.
• Contribute code upstream to Lance
  
  DB’s core repositories, including bug fixes, performance improvements, new features, and architectural refinements informed by customer use cases.
• Capture, distill, and communicate structured product feedback from customer engagements to product and core engineering teams, influencing roadmap and design decisions.
• Integrate Lance
  
  DB into existing data and AI infrastructure stacks, including Spark, Ray, and similar distributed processing frameworks.
• Diagnose and resolve complex issues involving distributed systems, cloud object storage, concurrency, and large‑scale data movement.
• Partner closely with product, core engineering, and GTM teams to ensure customer requirements translate into generalizable, reusable platform capabilities.
• Deliver technical deep dives, workshops, and proofs‑of‑concept for engineers and architects at customer organizations.

• Proven experience building, deploying, or operating distributed, cloud‑native databases or data platforms in production.
• Strong proficiency in Rust and Python, with a demonstrated ability to write performant, maintainable systems code.
• Hands‑on familiarity with data infrastructure technologies such as Apache Spark, Ray, or similar distributed compute and data processing frameworks.
• Experience integrating databases with batch and streaming data pipelines, ML workflows, or large‑scale analytics systems.
• Demonstrated ability to contribute directly to core product codebases, not just customer‑specific glue or scripts.
• Deep understanding of distributed systems concepts including sharding, replication, consistency, concurrency, and failure handling.
• Strong customer‑facing skills, with the ability to work directly with engineers, architects, and technical leaders to drive solutions from concept to production.

• Experience with vector databases, similarity search, or multimodal data systems.
• Prior contributions to open‑source databases, storage engines, or distributed systems projects.
• Familiarity with cloud platforms (AWS, GCP, Azure), Kubernetes, Terraform, and observability tooling.
• Experience with Apache Arrow‑based ecosystems, large‑scale ML data pipelines, or AI infrastructure stacks.

As a Forward Deployed Engineer at Lance

DB, you’ll work directly with cutting‑edge customers while shaping the core product itself. Your field experience will feed back into Lance

DB’s architecture, APIs, and performance roadmap, giving you a rare opportunity to influence both customer success and the evolution of next‑generation AI data infrastructure.