Staff Machine Learning Engineer

ELC Beauty LLC•New York, NY

1d•$119,300 - $196,600

About The Position

The Estée Lauder Companies Inc. is one of the world’s leading manufacturers, marketers, and sellers of quality skin care, makeup, fragrance, and hair care products, and is a steward of luxury and prestige brands globally. The company’s products are sold in approximately 150 countries and territories under brand names including: Estée Lauder, Aramis, Clinique, Lab Series, Origins, M·A·C, La Mer, Bobbi Brown Cosmetics, Aveda, Jo Malone London, Bumble and bumble, Darphin Paris, TOM FORD, Smashbox, AERIN Beauty, Le Labo, Editions de Parfums Frédéric Malle, GLAMGLOW, KILIAN PARIS, Too Faced, Dr.Jart+, the DECIEM family of brands, including The Ordinary and NIOD, and BALMAIN Beauty. As a Staff Machine Learning Engineer, you're a player-coach: a strong hands-on contributor who also sets standards, reviews others' work, authors the designs others build against, and mentors the team. You design, build, test, and deploy production AI products and the platform beneath them, and you shape how we build and integrate AI systems — working with engineers, scientists, product managers, and domain experts to turn ambiguous problems into reliable, scalable deliverables. The core of this role is engineering: agentic AI products and their platform, including stateful, transactional services where reliability and data-modeling rigor matter. Classical ML, statistics, and experimentation are valued and let us take on predictive and inference work when the roadmap calls for it — but you may spend long stretches purely on AI-product and platform engineering, so we're looking for someone energized by that work.

Requirements

BS/BA in a quantitative or technical field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering, Operations Research, Economics), or equivalent practical experience; a graduate degree is a plus.
5+ years of experience (3+ with a graduate degree) across Software Engineering, Machine Learning, and/or Data Science.
Strong general software engineering in Python — clean API and service design, data-structure and algorithm proficiency, and real testing discipline — with experience building and operating production-grade services, including stateful / transactional systems and databases (SQL and/or NoSQL).
Experience building and deploying LLM-powered or agentic systems in production (e.g., retrieval-augmented generation, agents and tool use, structured outputs, evaluation, safety) — or a strong production-ML background paired with a clear appetite to work in this space.
Experience deploying and operating systems on a major cloud platform (Google Cloud a plus) with sound data-processing practices at scale.
Uses agentic / AI-assisted development tools, or is eager to adopt them in earnest, with judgment about where they help and how to keep quality high.
A track record of mentorship, a continuous-learning mindset, a bias for simplicity, and a collaborative, shared-ownership working style.

Nice To Haves

Genuine classical ML / data science / statistics depth — statistical modeling, hypothesis testing and experimentation, causal inference and incrementality, and predictive modeling. We value this as a way to de-risk future modeling work; it is hard to acquire on the job, so we welcome it even when the immediate work is engineering-focused.
Hands-on experience in NLP, agent frameworks, or advanced statistical methods.
Familiarity with our stack: Google Cloud (Vertex AI, Cloud Run, BigQuery, Firestore, Pub/Sub), async Python (FastAPI), container-based CI/CD, and the Model Context Protocol (MCP).
Experience designing a technical roadmap and leading execution in a business environment.

Responsibilities

Design, build, test, and deploy production agentic / LLM-powered products end-to-end on Google Cloud, along with the shared platform, tooling, and harnesses beneath them.
Bring strong software-engineering rigor to our services — including stateful and transactional (OLTP) systems — across data modeling, concurrency, idempotency, reliability, observability, and testing.
Apply classical ML, statistics, and experimentation where the problem calls for it (predictive modeling, statistical inference, hypothesis testing, causal / incrementality measurement).
Build scalable tools that drive automation across domains such as recommendation and personalization, conversational and customer experiences, and measurement and incrementality.
Author design documents, uphold standards for clean code and documentation, and review others' designs and deliverables.
Mentor and grow engineers and scientists, and support our early-career and student mentorship efforts.
Partner with engineering, product, and domain-expert teams on major cross-functional automation, measurement, and modeling efforts.
At least 80% technical contributor, up to 20% leadership / management.
Actively participate in ELC's diversity and inclusion agenda.