Staff Machine Learning Engineer, CustomerLake (ML/LLM)

Description

Posting ID P-1607.

The company builds and runs a data intelligence platform that helps customers use deep data insights to improve their business. Over 20,000 organizations worldwide use it, including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500. The company is headquartered in San Francisco with 30+ offices worldwide.

The company was founded by engineers and is focused on customers. Its engineers work on problems from next-generation UI and UX for working with data to scaling services and infrastructure across millions of virtual machines.

You join the NYC Engineering office as one of its first engineers, on a small team building new products from scratch.

The team is building a customer data platform for companies whose data already sits on the platform but that have no in-house ML or AI team. The goal is to give them the personalization that large B2C and B2B brands have historically built with their own teams, such as recommendations, churn and lifetime-value modeling, and audience targeting.

This is a zero-to-one role that combines startup energy with the resources of a large tech company.

Responsibilities

• Assess ML and LLM methods for personalization use cases, advance the models and algorithms, and raise quality steadily over time.
• Examine individual traces of models running in production to understand how they reason, then tune them.
• Build the platform and evaluation framework that let customers measure real business results such as purchases, retention, and product usage instead of opens and clicks.
• Propose new directions and novel methods worth pursuing, not only refinements to what already exists.
• Work with product management, engineering, and design to turn unclear customer problems into scalable, trustworthy solutions.
• Set the technical foundation and best practices for ML and AI personalization as the area grows into several roles across products over the next 1-2 years.

Requirements

• 10+ years of engineering experience, covering the full cycle of shipping and improving ML and AI products.
• Hands-on experience building and evaluating ML models or LLM systems for real product or business needs. Your understanding should be practical, and you should be able to 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.
• Python and modern ML frameworks such as PyTorch, plus hands-on experience evaluating models and monitoring AI quality in production.
• Familiarity with LLMs and generative AI, including retrieval-augmented generation (RAG), prompt design, fine-tuning, and evaluation.
• A product mindset: you turn ambiguous customer problems into scrappy MVPs and iterate quickly using data and user feedback.
• High ownership and a bias for action in 0-to-1 environments. You make pragmatic trade-offs, work with incomplete information, and drive projects from idea through launch and adoption.
• Experience in martech, ideally in a go-to-market or business use case with an analytical rather than purely transactional angle.
• An academic or research background that can help the team innovate and develop novel methods.

Conditions

The local pay range is $190,000 - $253,400 USD. This is the expected range for non-commissionable roles, or on-target earnings for commissionable roles. Actual pay depends on factors such as job-related skills, depth of experience, certifications and training, and work location, and the company expects to use the full width of the range.

The total package may include eligibility for an annual performance bonus, equity, and the benefits listed below.

Candidates are considered without regard to protected characteristics, and if access to export-controlled technology or source code is required, the employer decides whether to seek a U.S. government license and may decline an applicant on that basis alone.

Benefits

• Comprehensive benefits and perks, with region-specific details available from the company.