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, MLOps, etc.
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, as well as participating in the larger ML SME community in Databricks
Collaborate cross-functionally 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, scale, and optimize customer data science workloads and apply best in class 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

Requirements

5+ years of hands-on industry ML experience in at least one of the following:
ML Engineer: Build and maintain 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/or teaching technical concepts to non-technical and technical audiences alike
Passion for collaboration, life-long learning, and driving business value 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.
This role can be remote, but we prefer that you be located in the job listing area and can travel up to 30% when needed.

Benefits

Health insurance
401(k) matching
Flexible working hours
Paid time off
Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningMLOpsnatural language processingvector databasesfine-tuning LLMsdeploying LLMsfeature engineeringmodel monitoringcloud infrastructuredrift monitoring

Soft Skills

communicationcollaborationtechnical teachingcustomer advocacyinfluenceproblem-solvinglife-long learningbusiness value driving

Certifications

graduate degree in quantitative discipline