Senior Solutions Engineer

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 [... source excerpt omitted ...] 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. Famili [... source excerpt omitted ...] n-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