Staff Machine Learning Engineer, Ads Measurement Modeling

Details

Reddit's Ads Measurement Org develops machine learning solutions for advertising measurement, including Identity Matching, Identity Graph, Utility Enhancement for Ads Privacy, Modeled Conversion, and related measurement modeling initiatives. The Measurement Modeling team works across ads measurement, reporting, experimentation, personalization, delivery, and safety systems.

The Staff ML Engineer will define the long-term technical direction and architecture for ads identity modeling, drive engineering quality and best practices, lead modeling initiatives and cross-organizational collaboration, and promote the use of state-of-the-art machine learning technology.

Responsibilities

• Lead the technical strategy and architecture for ads identity modeling and related ads measurement models.
• Design and train advanced machine learning models while balancing accuracy, scalability, privacy compliance, complexity, latency, and prediction quality.
• Oversee end-to-end machine learning workflows, including data ingestion, feature engineering, model training, evaluation, and deployment.
• Optimize machine learning systems for performance and cost.
• Partner with product management, data science, platform engineering, privacy, and legal teams to define roadmaps and long-term goals.
• Establish engineering best practices, code quality standards, and data governance guidelines for a maintainable and trustworthy identity graph.
• Mentor and coach junior engineers and foster technical excellence, innovation, and knowledge sharing.

Requirements

• At least 7 years of professional software engineering experience, including at least 3 years focused on machine-learning-driven systems at scale.
• Experience architecting and building ads measurement modeling solutions using advanced machine learning techniques.
• Strong knowledge of identifiers such as cookies, hashed emails, phone numbers, IP addresses, and user agents, and their use in identity resolution.
• Proficiency with machine learning frameworks such as TensorFlow and PyTorch, as well as libraries for feature engineering, model training, and inference.
• Understanding of large-scale data processing, distributed computing, and data infrastructure such as Spark, Kafka, Beam, and Flink.
• Proven technical leadership in cross-functional settings, including architectural decision-making and stakeholder influence across product, data science, privacy, and legal teams.
• Excellent communication, mentoring, and collaboration skills.

Benefits

• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefits supporting workspace, professional development, and caregiving.
• Family planning support.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Paid parental leave.
• Eligibility for equity in the form of restricted stock units.

The base salary range is $230,000–$322,000 USD. Final compensation may vary based on skills, experience, and other factors.