Asks for

The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”
The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”
The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”
The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”
The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”
The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”
The ad says“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.”
The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”
The ad says“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.”
The ad says“Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).”

Values

The ad says“Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).”
The ad says“Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).”
The ad says“Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).”
The ad says“Hands-on experience building or supporting vector search pipelines, RAG applications, feature stores, or multimodal AI architectures.”
The ad says“Familiarity with modern observability and monitoring stacks (Prometheus, Grafana, OpenTelemetry) for troubleshooting distributed workloads.”
The ad says“Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).”
The ad says“Hands-on experience building or supporting vector search pipelines, RAG applications, feature stores, or multimodal AI architectures.”
The ad says“Familiarity with modern observability and monitoring stacks (Prometheus, Grafana, OpenTelemetry) for troubleshooting distributed workloads.”
The ad says“Familiarity with modern observability and monitoring stacks (Prometheus, Grafana, OpenTelemetry) for troubleshooting distributed workloads.”
The ad says“Hands-on experience building or supporting vector search pipelines, RAG applications, feature stores, or multimodal AI architectures.”
The ad says“Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).”
The ad says“Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).”
The ad says“Hands-on experience building or supporting vector search pipelines, RAG applications, feature stores, or multimodal AI architectures.”

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 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 Do

Partner 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).

What We’re Looking For

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.

Nice to 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).