Engineering Manager, Ads ML Efficiency (ML)

TL;DR

Engineering Manager, Ads ML Efficiency (ML): Lead a dedicated Ads ML Efficiency function to make model training and inference faster, cheaper, safer, and more scalable with an accent on model optimization, GPU enablement, and efficiency guardrails across Ads ML. Focus on building profiling/benchmarking and load-testing tooling, driving measurable reductions in training time and online latency, and removing critical-path bottlenecks through cross-team alignment.

Company

Reddit is a community platform built on shared interests, trust, and open conversations.

Location

Location: Remote - United States

Salary: $230,000 - $322,000 USD

What you will do

• Hire, mentor, and retain ML engineers and systems-oriented engineers focused on model optimization and ML efficiency.
• Define the roadmap for training optimization, inference optimization, launch-readiness tooling, and reusable efficiency primitives across Ads ML.
• Drive measurable wins including reductions in model training time, online latency, serving cost, and infra-driven launch risk.
• Build and improve systems and tooling for profiling, benchmarking, load testing, observability, cost analysis, debugging, and efficiency certification.
• Partner with model owners and ML platform/serving owners to accelerate high-priority launches and remove bottlenecks to production.
• Establish engineering rigor around measurement, performance debugging, launch safety, and technical decision-making for efficiency work.

Requirements

• Deep ML engineering experience with hands-on understanding of training, serving, debugging, and optimization.
• Hands-on background improving training loops, serving systems, profiling workflows, model/inference efficiency, or GPU utilization.
• Proven managerial ability: building and leading teams, coaching engineers, managing delivery, and prioritizing under ambiguity.
• Fluency with production-scale distributed ML systems and tradeoffs across reliability, speed, cost, and scale.
• Ability to act as a service provider to modeling teams while building reusable systems (not only one-off optimizations).
• Strong communication skills to explain technical tradeoffs to engineers, PMs, and senior stakeholders.

Nice to have

• Experience with GPU training and serving migrations.
• Experience with PyTorch, distributed training frameworks, or kernel/performance optimization.
• Experience building efficiency benchmarking or launch certification frameworks.
• Experience in organizations where ML platform and applied modeling responsibilities are split across multiple teams.

Culture & Benefits

• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match and global benefit programs for lifestyle and professional development.
• Flexible vacation and paid volunteer time off, plus generous paid parental leave.
• Family planning support, gender-affirming care, and mental health & coaching benefits.
• Equity eligibility (restricted stock units) and possible commission depending on role and position.

Hiring process

• Interviews may be recorded, transcribed, and summarized by AI; opt-out is available prior to scheduled interviews.
• Interviews evaluate application information categories collected during the process.