Machine Learning Engineer, CustomerLake; ML​/LLM

Staff Machine Learning Engineer, Customer Lake (ML/LLM)

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best Data Intelligence Platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines.

And we're only getting started.

As one of the first engineers in the NYC Engineering office, you'll join a small, nimble team building new products from the ground up. We're building Customer Lake, the Customer Data Platform on Databricks, to bring enterprise-grade ML and AI personalization to every company whose data already lives on Databricks. The best B2C and B2B brands have historically relied on in-house ML/AI teams to power personalization, recommendations, churn and lifetime-value modeling, and audience targeting.

Our goal is to deliver that same capability to companies that don't have an in-house team but already have their data in order on Databricks. This is a true 0-to-1 environment, combining the excitement of a startup with the resources of a tech leader like Databricks.

The impact you'll have:

• Evaluate ML and LLM approaches for Customer Lake'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 Customer Lake 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

What we look for:

• 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 have:

• 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

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to…