Staff Machine Learning Engineer CustomerLake ML LLM
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will evaluate and improve ML and LLM approaches for personalization use cases. You will inspect production model behavior, build evaluation frameworks tied to business outcomes, develop scalable and trustworthy solutions with cross-functional partners, and establish the technical foundation and best practices for ML and AI personalization.
Requirements
- 10+ years of engineering experience shipping and improving ML or AI products
- Hands-on experience building and evaluating ML models or LLM systems for product or business use cases
- Experience with customer-behavior or transaction-based personalization, such as recommendations, targeting, churn, or lifetime-value modeling
- Proficiency in Python and modern ML frameworks such as PyTorch
- Experience with model evaluation and monitoring AI quality in production
- Familiarity with LLMs, generative AI, retrieval-augmented generation, prompt design, fine-tuning, and evaluation
- Ability to translate ambiguous customer problems into MVPs and iterate using data and user feedback
- Ability to drive projects from idea through launch and adoption
Responsibilities
- Evaluate and improve ML and LLM approaches for personalization use cases
- Inspect production model traces and tune model behavior
- Build a platform and evaluation framework tied to business value
- Identify and pursue novel methods
- Partner with product management, engineering, and design on scalable solutions
- Set technical foundations and best practices for ML and AI personalization
Benefits
- Eligibility for annual performance bonus
- Equity