Sr. Data Scientist/Machine Learning Engineer - San Francisco, California

CSQ325R50

While candidates in the listed locations are encouraged for this role, we are open to remote candidates in other locations.

The Machine Learning (ML) Practice team is a specialized customer-facing ML team at Databricks facing an increasing demand for Large Language Model (LLM)-based solutions. We deliver professional services engagements to help customers build and improve ML pipelines, and put those pipelines into production. We work with customers to help them shape their long-term initiatives working alongside engineering, product, and developer relations, and internal subject matter expert (SME) teams. The ideal candidate will enjoy being part of a broader team of technologists that love empowering customers, collaborating with teammates, and satisfying your curiosity working with the latest trends in LLMs, MLOps, and ML.

The impact you will have:

• Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
• Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains
• Advise data teams on several data science such as architecture, tooling, and best practices
• Present at conferences such as Data+AI Summit
• Provide technical mentorship to the larger ML Subject Matter Expert community in Databricks
• Collaborate with the product and engineering teams to define priorities and influence the product roadmap

What we look for:

• 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
• 4+ years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, scikit-learn, gensim, nltk, and TensorFlow/PyTorch
• Experience building production-grade machine learning deployments on …