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DatabricksDatabricksNew York, NY

Staff Software Engineer

Staff ML Engineer building CustomerLake, Databricks' Customer Data Platform for enterprise ML/AI personalization, recommendations, churn, and LTV modeling. Requires 10+ years shipping production ML/LLM systems with strong product mindset in 0-to-1 environments.

192k – 260k
On-site10+ YOEML Engineering

About the role

Impact

  • Evaluate ML and LLM approaches for CustomerLake's personalization use cases, push the models and algorithms forward, and continuously improve quality over time.
  • Go deep on how models behave in production: inspect individual traces, understand how the models reason, and tune and improve from there.
  • Build the platform and evaluation framework that let CustomerLake customers optimize for real business value such as purchases, retention, and product usage, not vanity metrics like email opens and clicks.
  • Push the team toward new directions and novel methods worth tackling, not just optimizing what already exists.
  • Partner closely with product management, engineering, and design to turn ambiguous customer problems into scalable, trustworthy solutions.
  • Set the technical foundation and best practices for our ML/AI personalization work as we grow this into several roles across our products over the next 1-2 years.

Requirements

  • 10+ years of engineering experience, with a strong foundation across the full loop of shipping and improving ML/AI products.
  • Hands-on experience building and evaluating ML models and/or LLM systems for real product or business use cases; your understanding is practical, not purely academic, and you can make models work well inside a product.
  • Experience with personalization based on customer behavior (ideal) or transactions (acceptable), such as recommendations, targeting, churn, or lifetime-value modeling.
  • Proficiency in Python and modern ML frameworks (e.g., PyTorch), with hands-on experience in model evaluation and monitoring AI quality in production.
  • Familiarity with LLMs and generative AI, including techniques like retrieval-augmented generation (RAG), prompt design, fine-tuning, and evaluation.
  • A demonstrated product mindset, with the ability to translate ambiguous customer problems into scrappy MVPs and iterate quickly based on data and user feedback.
  • High ownership and bias for action in 0-to-1 environments: comfortable making pragmatic trade-offs, operating with incomplete information, and driving projects from idea through launch and adoption.

Nice-to-Haves

  • Experience in martech, ideally a go-to-market or business use case with an analytical (rather than purely transactional) angle.
  • An academic or research background that can help us innovate and develop novel methods.

Skills

PythonPyTorchLLMsRAGMachine LearningGenerative AIModel EvaluationPrompt DesignFine-TuningPersonalization ModelsRecommendationsChurn Modeling

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