About LanceDB
LanceDB 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.
You must be based out of the San Francisco Bay Area, and be willing to travel to customer sites as needed. This position is only available to candidates that fulfill this criteria.
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.
