Engineering Manager, Ads ML Efficiency
Core
Leading a dedicated function to make model training and inference for Ads ML faster, cheaper, safer, and more scalable.
Role type
Engineering Manager, Ads ML Efficiency
Builds
Model optimization, training efficiency, GPU enablement, load testing, model performance tooling, and efficiency guardrails across Ads ML.
Domain
Internet / Advertising / Machine Learning
Deliverable
production ML models
Required skills
ML engineering, systems optimization, team leadership, distributed systems, cross-functional collaboration
Preferred skills
Ads ranking, recommender systems, GPU training and serving migrations, PyTorch, distributed training frameworks, kernel/performance optimization, efficiency benchmarking
Technologies
PyTorch, GPU
Responsibilities
Hire, mentor, and retain a high-performing team of ML engineers; Define the roadmap for training and inference optimization; Drive reductions in model training time, online latency, serving cost, and infra-driven launch risk; Guide the development of profiling, benchmarking, load testing, observability, cost analysis, debugging, and efficiency certification systems; Partner with model owners and platform teams to accelerate high-priority launches and remove bottlenecks; Balance near-term optimization work with medium-term platformization and automation; Work closely with MLP, AMP, Ranking, and serving teams to clarify boundaries and upstream generic wins; Establish engineering rigor around measurement, performance debugging, launch safety, and technical decision-making.
Seniority
Manager, hands-on IC with team leadership
