Staff Machine Learning Engineer, CustomerLake (ML/LLM)
$192k - $260k • New York City, New York
Posted 8d 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 a Staff Machine Learning Engineer to join a new, nimble team in NYC focused on building the Customer Data Platform on Databricks. This role is a unique opportunity to be part of a 0-to-1 environment, combining startup excitement with the resources of a tech leader. You will be instrumental in bringing enterprise-grade ML and AI personalization capabilities to companies that already have their data on Databricks, enabling them to power personalization, recommendations, churn and lifetime-value modeling, and audience targeting without needing an in-house ML/AI team. This is a foundational role where you will set the technical direction and best practices for ML/AI personalization work.
Responsibilities
- Evaluate ML and LLM approaches for personalization use cases and drive model improvements.
- Analyze model behavior in production, inspect traces, and tune models for better reasoning.
- Build the platform and evaluation framework to help customers optimize for real 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 technical foundations and best practices for ML/AI personalization work.
Requirements
- 10+ years of engineering experience with a strong track record 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 (e.g., 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, including RAG, prompt design, fine-tuning, and evaluation.
- Demonstrated product mindset with the ability to translate ambiguous problems into MVPs.
- 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 (PTO)