Engineering Manager, Ads ML Efficiency

🕒 June 22

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Reddit, Inc.

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2005

💼 Consulting

📣 Marketing

📱 Media

Consulting • Marketing • Media

Reddit, Inc. is a social media platform that acts as a hub for thousands of communities, where users can engage in diverse conversations ranging from breaking news to niche interests. It enables users to post, comment, and vote on content, fostering a vibrant online community. Millions of people globally connect and share their passions on Reddit, creating a dynamic environment for authentic human interaction.

📋 Description

• Hire, mentor, and retain a high-performing team of ML engineers/systems-oriented engineers working 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 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 from the path to production. • Balance near-term white-glove optimization work with medium-term platformization and automation. • Work closely with MLP, AMP, Ranking, and serving teams to clarify boundaries, upstream generic wins, and keep Ads needs on track. • Establish engineering rigor around measurement, performance debugging, launch safety, and technical decision-making for efficiency work.

🎯 Requirements

• Deep ML Engineering Experience: The candidate should have been close to the models themselves and understand training, serving, debugging, and optimization in depth. • Hands-on Optimization Background: Direct experience improving training loops, serving systems, profiling workflows, model/inference efficiency, or GPU utilization. • Strong Managerial Ability: Experience building and leading teams, coaching engineers, managing delivery, and making prioritization tradeoffs under ambiguity. • Distributed Systems Fluency: Proven ability to reason about production-scale ML systems and the tradeoffs that govern reliability, speed, cost, and scale. • Customer and Platform Instincts: Able to work as a service provider to modeling teams while still building reusable systems rather than only heroic one-offs. • Strong Communication: Can explain technical tradeoffs clearly to engineers, PMs, and senior stakeholders. • Ads experience: Experience in ads ranking, recommender systems, marketplace ML, or adjacent production ML domains is strongly preferred.

🏖️ Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs • 401k with Employer Match • Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support • Family Planning Support • Gender-Affirming Care • Mental Health & Coaching Benefits • Flexible Vacation & Paid Volunteer Time Off • Generous Paid Parental Leave