Remote Specialist Solutions Architect

Summary

Join Databricks as a Specialist Solutions Architect (SSA) - ML Engineering to be the trusted technical ML expert for customers and the Field Engineering organization. You will work with Solution Architects to guide customers in architecting production-grade ML applications on Databricks, while aligning their technical roadmap with the evolving Databricks Data Intelligence Platform.

Requirements

• 5+ years of hands-on industry ML experience in at least one of the following
• ML Engineer: Develop production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring
• Data Scientist: Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving our values through ML

Responsibilities

• Architect production level ML workloads for customers using our unified platform, including end-to-end ML pipelines, training/inference optimization, integration with cloud-native services and MLOps
• Provide advanced technical support to Solution Architects during the technical sale ranging from feature engineering, training, tracking, serving to model monitoring all within a single platform, and participating in the larger ML SME community in Databricks
• Collaborate with the product and engineering teams to represent the voice of the customer, define priorities and influence the product roadmap, helping with the adoption of Databricks’ ML offerings
• Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains
• Serve as the trusted technical advisor for customers developing GenAI solutions, such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, content generation, and monitoring

Preferred Qualifications

• [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role
• [Preferred] Experience working with Apache Spark™ to process large-scale distributed datasets

Benefits

• Health insurance
• Retirement benefits
• Paid time off
• Remote work, flexible hours
• Life and disability insurance
• Bonuses and incentives
• Professional development opportunities
• Wellness programs
• Family and parental leave