Engineering Manager (Ads ML Efficiency)

Role overview

Join Reddit as an Engineering Manager for Ads ML Efficiency. You will lead a team responsible for improving the efficiency of Ads ML systems across training, inference, GPU enablement, load/performance testing, and model performance tooling.

Responsibilities

• Lead and manage an Ads ML efficiency team (optimization, training efficiency, GPU enablement, load testing, and performance tooling).
• Define and execute roadmaps for:
  • training optimization
  • inference optimization
  • launch-readiness tooling
  • reusable efficiency primitives across Ads ML
• Drive measurable reductions in:
  • model training time
  • online latency
  • serving cost
  • infra-driven launch risk
• Partner with model owners and platform teams to deliver improvements.

Requirements

• Deep ML engineering experience, including hands-on understanding of training, serving, debugging, and optimization.
• Strong people leadership: building/leads teams, coaching engineers, delivery management, and prioritization under ambiguity.
• Fluency in distributed systems and production-scale ML tradeoffs (reliability, speed, cost, scale).
• Ability to act with customer/platform instincts: support modeling teams while building reusable systems.
• Clear technical communication with engineers, PMs, and senior stakeholders.
• Hands-on optimization background in training loops, serving systems, profiling workflows, model/inference efficiency, or GPU utilization.

Nice to have

• Ads or recommender/marketplace ML experience (ads ranking, recommender systems, production ML adjacent domains).
• Experience with GPU training/serving migrations.
• PyTorch and distributed training frameworks; familiarity with kernel/performance optimization.
• Experience building efficiency benchmarking or launch certification frameworks.
• Experience working where ML platform and applied modeling are split across multiple teams.

Location / Remote

• Full remote