Senior Data Scientist / Machine Learning Engineer - GenAI & LLM

About the Role

The Machine Learning (ML) Practice team at Databricks is a highly specialized customer-facing ML team experiencing increasing demand for Large Language Model (LLM)-based solutions. We deliver professional services engagements to help our customers build, scale, and optimize ML pipelines, and productionize these pipelines. We work cross-functionally to shape long-term strategic priorities with engineering, product, and developer relations, and support internal subject matter expert (SME) teams. We are looking for individuals with strong, unique specializations to improve the overall strength of the team. This role is ideal for those who enjoy working with customers and teammates, and are curious about the latest trends in LLMs, MLOps, and ML more broadly.

The Impact You Will Have

• Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repositories, querying structured data with natural language, and content generation.
• Build, scale, and optimize customer data science workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains.
• Advise data teams on various data science aspects such as architecture, tooling, and best practices.
• Present at conferences such as Data+AI Summit.
• Provide technical mentorship to the larger ML SME community in Databricks.
• Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap.

What We Look For

• Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI.
• 5+ years of hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch.
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP.
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience.
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike.
• Passion for collaboration, life-long learning, and driving business value through ML.
• [Preferred] Experience working with Databricks & Apache Spark to process large-scale distributed datasets.