LanceDB is a high-performance, developer-friendly, open-source data lake built for multimodal AI. From hyper-scalable vector search at a multi-billion scale to advanced retrieval for RAG, streaming training data, and real-time analytics, LanceDB powers some of the most groundbreaking applications and challenging AI infrastructure requirements today.
We are building the next generation of intelligent, data-driven systems—and we’re looking for a strategic, customer-focused engineer to build, enable, and scale our global partner ecosystem.
About the RoleAs a Senior Partner Solutions Architect (PSA), you will sit at the intersection of product engineering, business development, and customer success. You will be the trusted technical advisor to LanceDB’s strategic partner ecosystem, including global System Integrators (SIs), Cloud Service Providers (AWS, GCP, Azure), Technology Alliances, and AI/ML orchestration platforms.
Your mission is to enable, empower, and unblock our partners so they can successfully architect, deploy, and scale LanceDB solutions on behalf of their enterprise customers. This role requires a unique blend of deep technical grit (distributed systems, AI/ML pipelines) and exceptional relationship-building skills to drive mutual growth and technical excellence.
What You'll DoPartner Enablement & Training: Lead technical onboarding, training programs, and certifications for partner engineers and architects. Equip them to independently deliver and support LanceDB implementations.
Joint Architecture & Co-Delivery: Partner with alliances and field teams to support high-value proofs-of-concept (PoCs), design reviews, and architectural validations for complex, cloud-native enterprise environments.
Scalable Technical Content: Author and maintain partner-facing technical assets, including production-ready reference architectures, integration guides, deployment blueprints (Terraform, Docker), and sample code.
Product & Ecosystem Advocate: Act as the primary technical liaison between our partners and LanceDB’s internal Product and Engineering teams. Synthesize partner-sourced feedback and feature requests to directly influence our roadmap.
Go-To-Market (GTM) Collaboration: Participate in joint business planning and support regional technical marketing events, hackathons, and campaigns alongside channel account managers.
Thought Leadership: Drive ecosystem adoption by sharing best practices through technical blogs, whitepapers, open-source contributions, and presentations at major industry conferences (e.g., AWS re:Invent, AI meetups).
Must-Have Qualifications:
Experience: 10+ years of professional experience in customer- or partner-facing technical roles (e.g., Solutions Architecture, Partner Engineering, Sales Engineering, or ML Infrastructure), ideally supporting data platforms or distributed systems.
Ecosystem Familiarity: Proven track record working with or within a partner ecosystem (SIs, cloud providers, or technology alliances) with a firm grasp of how partners take solutions to market.
Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).
Programming Skills: Strong proficiency in Python and a willingness to dive into Rust (or vice versa) to read, debug, and write production-grade integration code or SDK extensions.
Communication: Exceptional presentation and communication skills. You can translate complex data infrastructure into actionable solutions for audiences ranging from partner developers to C-level executives.
Startup Agility: A self-starter mindset with the ability to thrive and operate autonomously in fast-moving, ambiguous environments.
Bonus Points If You Have:
Hands-on experience building or supporting vector search pipelines, RAG applications, feature stores, or multimodal AI architectures.
Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).
Familiarity with modern observability and monitoring stacks (Prometheus, Grafana, OpenTelemetry) for troubleshooting distributed workloads.
Active contributions to open-source communities or a portfolio of developer-facing technical content (blogs, tutorials, GitHub repositories).
You’ll join a world-class team of open-source builders working at the absolute forefront of AI infrastructure. In this role, you won't just support existing workflows—you will actively define how the entire AI ecosystem integrates with, deploys, and scales LanceDB across the enterprise.
We offer competitive compensation, comprehensive benefits, and a highly collaborative culture dedicated to developer experience and engineering excellence.
Skills Required
- 10+ years in customer- or partner-facing technical roles (Solutions Architecture, Partner/ Sales Engineering, ML Infrastructure)
- Proven experience working with partner ecosystems (System Integrators, cloud providers, technology alliances)
- Deep knowledge of distributed systems (sharding, replication, partitioning, performance tuning)
- Experience with container orchestration and cloud object storage (Kubernetes)
- Strong proficiency in Python and willingness to read, debug, and write code in Rust
- Exceptional presentation and communication skills for technical and executive audiences
- Ability to operate autonomously in a fast-paced startup environment
What We Do
LanceDB is a developer-friendly, open source database 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.
