Senior Staff Machine Learning Engineer, Ads Measurement

Position Overview

• Location Type: Remote
• Employment Type: Full-time
• Salary: Not specified

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The Ads Measurement Org is dedicated to enhancing the efficiency and user-friendliness of our advertising platform. We specialize in privacy enhancing technologies, identity resolution, and modeled conversions.

We’re looking for a hands-on Senior Staff Machine Learning Engineer with deep expertise in identity resolution and graph-based systems, who can lead end-to-end Ads initiatives across engineering and cross-functional teams. You will drive architectural decisions, mentor your peers, and dive into technical challenges directly when the team needs it. Our diverse group of engineers, product managers, data scientists, and ads specialists is excited to welcome you on board!

Requirements

• Experience: 8+ years of professional software engineering experience, with at least 3+ years focused on ML-driven systems at scale.
• Identity Graphs: Demonstrated experience architecting and building identity graphs, device graphs, or similar identity matching solutions leveraging probabilistic techniques.
• Identifiers: Strong knowledge of various identifiers (cookies, hashed emails, phone numbers, IP addresses, user agents) and their use in identity resolution.
• Machine Learning Frameworks: Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries for feature engineering, model training, and inference.
• Data Infrastructure: Solid understanding of large-scale data processing, distributed computing, and data infrastructure (e.g., Spark, Kafka, Beam, Flink).
• Leadership: Proven technical leadership in cross-functional settings, driving architectural decisions and influencing stakeholders (product, data science, privacy, legal).
• Communication & Collaboration: Excellent communication, mentoring, and collaboration skills to align teams on a long-term vision for identity resolution.

Responsibilities

• Technical Strategy: Lead the technical strategy and architecture for the company’s identity resolution system, ensuring accuracy, scalability, and compliance with privacy requirements.
• ML Model Development: Develop and refine ML models for probabilistic user matching using multiple identifiers (cookies, IP addresses, hashed emails, phone numbers, user agents).
• ML Workflow Management: Oversee end-to-end ML workflows—from data ingestion and feature engineering to model training, evaluation, and deployment—optimizing for performance and cost.
• Cross-Functional Collaboration: Partner with cross-functional teams (e.g., product management, data science, platform engineering, privacy, legal) to define the roadmap and set long-term goals.
• Best Practices & Governance: Establish engineering best practices, code quality standards, and data governance guidelines to ensure maintainability and trustworthiness of the identity system.

Application Instructions

• Not specified.