Staff Machine Learning Engineer, Ads ML Efficiency
Core
Build infrastructure, tooling, and optimization systems to enable efficient training, evaluation, deployment, and operation of machine learning models at scale.
Role type
Staff Machine Learning Engineer (ML Infrastructure & Efficiency)
Builds
ML training and inference infrastructure, optimization tooling, benchmarking frameworks, and performance dashboards.
Domain
Internet / Machine Learning Infrastructure / Systems Engineering
Deliverable
infrastructure
Required skills
Python, systems languages (Go/C++/Rust/Java), distributed systems, ML infrastructure, performance engineering, debugging, profiling
Preferred skills
large-scale recommendation/ranking/generative AI, distributed training frameworks (PyTorch Distributed/Ray/Spark), GPU architecture, cloud cost optimization
Technologies
PyTorch Distributed, Ray, Tensorflow, Spark
Responsibilities
Design systems to improve ML training and inference efficiency; develop tooling for debugging and monitoring; optimize GPU and resource utilization; partner with teams to identify bottlenecks; build benchmarking frameworks; optimize distributed training and serving architectures; lead cross-functional productivity initiatives; drive technical strategy for platform scalability and cost efficiency.
Seniority
Staff, hands-on IC with strategic impact
