Senior Machine Learning Engineer, Ads ML Platform

Role Description

We’re building a scalable feature platform that powers Ads ML by making high-quality features and training datasets easy to build, share, and maintain. Our small but growing team works on projects like:

• Batch & realtime feature management platform
• Training set generation platform
• Sequence features platform
• Agentic and automated ML workflows for feature lifecycle management

We are looking for an engineer with deep experience in building high-scale data infrastructure and ML platforms to help evolve and scale our feature management systems.

What You’ll Do:

• Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage.
• Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use.
• Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning.
• Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems.
• Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation, and feature lifecycle management.
• Drive architecture and technical direction for high-scale ML platform infrastructure powering ads ranking, targeting, and optimization systems.
• Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives.

Qualifications

• 5+ years in infrastructure/platform engineering or large-scale distributed systems.
• 2+ years of hands-on experience building or operating ML platform infrastructure and production ML systems.
• Proficiency with large-scale feature computation frameworks (Spark, PySpark, or Scala).
• Expertise in distributed systems (scaling, partitioning, fault tolerance, caching).
• Experience building intelligent automation or agentic workflows for ML systems is a strong plus, including areas such as automated pipeline management, feature recommendations, evaluation systems, or AI-assisted developer tooling.
• Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus.

Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave