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Staff Machine Learning Engineer

Estee Lauder New York, NY

New York, NY

Today

$119k - $197k/yr Full time Backend Developer Agentic Ai AI

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Job Description

The Staff Machine Learning Engineer (player-coach) will design, build, test, and deploy production AI products and the underlying platform that enables agentic, LLM-powered capabilities on Google Cloud. The role combines applied machine learning with software engineering rigor, including support for stateful and transactional systems, and includes mentoring responsibilities.

Key Responsibilities

Design, build, test, and deploy production agentic and LLM-powered products end-to-end on Google Cloud, including the shared platform, tooling, and harnesses beneath them.
Apply strong software-engineering rigor to services that involve stateful and transactional (OLTP) systems, with focus on data modeling, concurrency, idempotency, reliability, observability, and testing.
Use classical ML, statistics, and experimentation when appropriate, including predictive modeling, statistical inference, hypothesis testing, and causal or incrementality measurement.
Build scalable automation tools for domains such as recommendation and personalization, conversational and customer experiences, and measurement and incrementality.
Produce design documents, maintain standards for clean code and documentation, and review designs and deliverables.
Mentor and develop engineers and scientists, including participation in early-career and student mentorship efforts.
Collaborate with engineering, product, and domain-expert teams on cross-functional automation, measurement, and modeling initiatives.
Participate in ELC’s diversity and inclusion agenda.

Required Qualifications

BS/BA in a quantitative or technical field (for example, Computer Science, Statistics, Mathematics, Physics, Engineering, Operations Research, Economics) or equivalent practical experience; a graduate degree is a plus.
5+ years of experience (or 3+ years with a graduate degree) across Software Engineering, Machine Learning, and/or Data Science.
Strong general software engineering in Python, including clean API and service design, data-structures and algorithms, and a disciplined approach to testing. Experience building and operating production-grade services, including stateful/transactional systems and databases using SQL and/or NoSQL.
Production experience deploying and operating LLM-powered or agentic systems (examples: retrieval-augmented generation, agents and tool use, structured outputs, evaluation, safety) or a strong production ML background with a clear interest in transitioning into this space.
Experience deploying and operating systems on a major cloud platform, with Google Cloud as a plus, and sound data-processing practices at scale.
Experience using agentic/AI-assisted development tools, or a strong willingness to adopt them with judgment to protect quality.
A track record of mentorship, continuous learning mindset, bias for simplicity, and collaborative shared-ownership working style.

Technologies

Python
Google Cloud
SQL, NoSQL
Retrieval-augmented generation
LLM-powered systems
Agentic systems
Vertex AI
Cloud Run
BigQuery
Firestore
Pub/Sub
FastAPI
Container-based CI/CD
Model Context Protocol (MCP)

Benefits

Health insurance coverage (medical, dental, and vision insurance)
Wellness and family support programs
Life and disability insurance
Retirement savings plans
Paid leave programs
Education-related programs
Paid holidays and vacation time
Bonus program (highly competitive), with the possibility for overachievement
Participation in the share incentive plan

Preferred / Strong Plus

Depth in classical ML, data science, and statistics, including statistical modeling, hypothesis testing and experimentation, causal inference and incrementality, and predictive modeling.
Hands-on experience in NLP, agent frameworks, or advanced statistical methods.
Familiarity with the stack, including 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.

Compensation and Pay Range

The anticipated base salary range is $119,300.00 to $196,600.00 per year.
Exact compensation depends on factors such as experience, skills, education, and budget.
Salary range may vary based on geographic location.
In addition to base salary, the position is eligible for participation in a highly competitive bonus program and the share incentive plan.

Location

New York, NY (onsite)

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2mo ago

Senior Data Engineer - Analytics

Everforth CyberCoders Philadelphia, PA 🇺🇸 Remote

Philadelphia, PA

2mo ago Closed

Full time Senior Analytics Artificial Intelligence

This position is no longer accepting applications (closed Jul 17, 2026).

Job Description

Senior Data Engineer - Analytics, 100% remote, based in Philadelphia, PA, focused on scalable analytics infrastructure on Google Cloud Platform to power dashboards, ML features, and downstream systems.

Responsibilities

Design, develop, and maintain ELT/ETL data transformation pipelines using dbt Core and SQL targeting BigQuery.
Author modular, tested SQL models and Python-based data transformations to support analytics, reporting, and machine learning feature generation.
Implement data quality checks, lineage, and observability to ensure reliable analytics outputs and SLAs.
Collaborate with product, analytics, and ML teams to define metric definitions and translate business requirements into performant data models.
Build and maintain RESTful APIs and integrations to surface curated datasets and features for internal and external consumers; integrate LLM APIs where applicable.
Deploy and monitor data services and lightweight API endpoints on GCP, leveraging Cloud Run and other serverless infrastructure when appropriate.
Optimize performance and cost for BigQuery workloads through partitioning, clustering, and query tuning.
Document data models, transformation logic, and operational runbooks; mentor teammates on best practices for dbt, SQL, and analytics engineering.

Requirements

3+ years of experience in analytics engineering, data engineering, or a related role building analytics pipelines and data models.
Experience working with Healthcare Claims Data.
Expert proficiency in SQL and strong experience with Python for data transformation, orchestration, or testing.
Proven experience using dbt Core to build modular, tested analytics transformations and manage deployments.
Solid experience with Google Cloud Platform, especially BigQuery, including query optimization and cost management.
Experience building and integrating APIs; familiarity with LLM APIs and integrating large language model outputs into analytics or product workflows.
Strong understanding of data modeling concepts, ETL/ELT patterns, data quality practices, and observability.
Excellent communication skills and ability to collaborate across cross-functional teams to operationalize analytics.
Nice to have: hands-on experience with Cloud Run, Vertex AI, and FastAPI for serving data or ML features; domain knowledge of healthcare claims and related data models.
Work authorization: Must be currently authorized to work in the United States without the need for sponsorship for a non-immigrant visa.

Technologies

dbt Core
SQL
BigQuery
Python
Google Cloud Platform (GCP)
Cloud Run
Vertex AI
FastAPI
LLM APIs
RESTful APIs

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Staff Machine Learning Engineer

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