Staff Machine Learning Engineer, CustomerLake (ML/LLM)
$190k - $253k • New York City, New York
Posted 9h ago
Job Location
New York City, New York
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
Databricks is seeking an engineer to join a new, nimble team in NYC to build CustomerLake, the Customer Data Platform on Databricks. This platform aims to bring enterprise-grade ML and AI personalization to companies whose data already resides on Databricks, enabling them to power personalization, recommendations, churn, and lifetime-value modeling without an in-house ML/AI team. This is a unique opportunity to work in a 0-to-1 environment, combining startup excitement with the resources of a major tech company.
Responsibilities
- Evaluate ML and LLM approaches for personalization use cases and improve model quality.
- Analyze model behavior in production, inspect traces, understand reasoning, and tune models.
- Build the platform and evaluation framework for customers to optimize for business value.
- Propose new directions and novel methods for the team to explore.
- Collaborate with product management, engineering, and design to solve customer problems.
- Establish the technical foundation and best practices for ML/AI personalization work.
Requirements
- 10+ years of engineering experience with a strong background in shipping and improving ML/AI products.
- Hands-on experience building and evaluating ML models or LLM systems for real-world use cases.
- Experience with personalization based on customer behavior or transactions (recommendations, targeting, churn, lifetime-value modeling).
- Proficiency in Python and modern ML frameworks like PyTorch.
- Experience in model evaluation and monitoring AI quality in production.
- Familiarity with LLMs and generative AI techniques such as RAG, prompt design, fine-tuning, and evaluation.
- Demonstrated product mindset with the ability to translate ambiguous problems into MVPs and iterate quickly.
- High ownership and bias for action in 0-to-1 environments, comfortable with trade-offs and incomplete information.
Benefits
- Eligibility for annual performance bonus
- Equity
- Health insurance
- Paid time off