Staff Machine Learning Engineer, Ads Measurement Modeling

We are looking for an IC5 Staff ML Engineer of Ads Identity Modeling, to define long term direction and drive architecture evolution, be responsible for engineering quality and enforce best practice, lead new exploration and cross-org collaboration in new modeling initiatives, and champion ML/AI innovation to ensure the solutions utilize SOTA ML technology.

Our diverse group of engineers, product managers, data scientists, and ads specialists is excited to welcome you on board!

- 7+ years of professional software engineering experience, with at least 3+ years focused on ML-driven systems at scale

- Demonstrated experience architecting and building ads measurement modeling solutions leveraging advanced machine learning techniques

- Strong knowledge of various identifiers (cookies, hashed emails, phone numbers, IP addresses, user agents) and their use in identity resolution

- Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries for feature engineering, model training, and inference

- Solid understanding of large-scale data processing, distributed computing, and data infrastructure (e.g., Spark, Kafka, Beam, Flink)

- Proven technical leadership in cross-functional settings, driving architectural decisions and influencing stakeholders (product, data science, privacy, legal)

- Excellent communication, mentoring, and collaboration skills to align teams on a long-term vision for identity resolution

- Lead the technical strategy and architecture for our company's ads identity modeling solutions and other related ads measurement models

- Design and train advanced ML models while ensuring accuracy, scalability, and compliance with privacy requirements, managing trade-offs between complexity, latency, and prediction quality

- Oversee end-to-end ML workflows-from data ingestion and feature engineering to model training, evaluation, and deployment-optimizing for performance and cost

- Partner with cross-functional teams (e.g., product management, data science, platform engineering, privacy, legal) to define the roadmap and set long-term goals

- Establish engineering best practices, code quality standards, and data governance guidelines to ensure maintainability and trustworthiness of the identity graph

- Mentor and coach junior engineers, fostering a culture of innovation, technical excellence, and knowledge sharing across the organization