Senior Solutions Engineer

• 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.
• 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.
• 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.

None flagged.