This role can be remote.

As a Specialist Solutions Architect (SSA) - Data Science & ML, you will be the trusted technical ML expert to both Databricks 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. You will continue to strengthen your technical skills through applying the latest technologies in GenAI, LLMOps, and ML, while expanding your impact through mentorship and establishing yourself as a ML expert.

The impact you will have:

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

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

[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

Can meet expectations for technical training and role-specific outcomes within 3 months of hire

Can travel up to 30% when needed