About the job
As a Staff Software Engineer on the Feature Platform team, you'll play a critical role in building the infrastructure that powers machine learning, experimentation, and optimization across our ads ecosystem. You'll design and operate the systems that transform high-volume event data into production-grade feature datasets used for bidding, attribution, and ranking — working at the intersection of distributed systems, platform engineering, and ML infrastructure.
Responsibilities
Design, build, and operate scalable, production-grade data pipeline systems and curated feature datasets powering ads optimization and MLOwn end-to-end offline data flows from raw event ingestion to feature-ready datasets, with strong emphasis on correctness, reproducibility, and SLA complianceDevelop and maintain large-scale batch and streaming systems (Python / Java / SQL) with a strong focus on performance, cost-efficiency, and reliabilityBuild and contribute to our Feature Store platform, including integration with the high-throughput online serving layer (Go-based services)Translate complex product and monetization logic into well-engineered, extensible systems serving analytics and machine learning use casesDrive improvements in observability, testing frameworks, and quality standards across the platformLead architectural decisions and engineering best practices within the Feature Platform team
Qualifications
Minimum
Strong software engineering fundamentals with deep experience designing and operating large-scale distributed systems in productionHands-on experience building production-grade ETL/ELT pipelines using Python, Java, SQL, or similar technologiesExperience with distributed processing frameworks such as Spark or Flink in both batch and streaming modes, including performance tuning and parallel computationUnderstanding of how offline data systems integrate with online serving layers — feature stores, low-latency APIs, and real-time systemsExperience with cloud-native environments, containerized systems, Kubernetes, and workflow orchestration toolsStrong ownership mindset — focused on correctness, observability, and long-term maintainability
Preferred
Experience with ML infrastructure, feature stores, or model training pipelinesBackground in ads, attribution, or monetization systemsFamiliarity with experimentation and metrics infrastructureExposure to high-scale backend or platform engineeringExperience with Go is a plus, particularly for collaboration on high-throughput feature serving services