Engineering Manager, Ads ML Efficiency

Reddit Inc.

Job Overview

Location

Remote - United States

Job Type

Full-time

Full Job Description

📋 Description

• Lead and grow a high-performing team of ML and systems-oriented engineers focused on Ads ML efficiency, including hiring, mentoring, and retention.
• Define and own the technical roadmap for training optimization, inference optimization, launch-readiness tooling, and reusable efficiency primitives across Reddit’s Ads ML systems.
• Drive measurable reductions in model training time, online latency, serving costs, and infrastructure-driven launch risk through targeted efficiency initiatives.
• Guide the development of profiling, benchmarking, load testing, observability, cost analysis, debugging, and efficiency certification systems to enable scalable, repeatable improvements.
• Partner directly with model owners, ML Platform (MLP), Ads Modeling Platform (AMP), Ranking, and serving teams to identify and remove critical bottlenecks in the path to production.
• Balance immediate, high-impact optimization work with medium-term platformization and automation efforts to ensure sustainable efficiency gains.
• Establish engineering rigor around performance measurement, debugging, launch safety, and technical decision-making for all efficiency-related work.
• Build cross-functional alignment (XFN) with platform and modeling teams to clarify ownership, upstream generic wins, and ensure Ads ML needs remain prioritized.
• Operate in the critical path of high-priority model launches, ensuring efficiency constraints are addressed early and systematically.
• Shape the evolution of the Ads ML Efficiency function by evolving it from a white-glove optimization team into a scalable, automated platform function.
• Communicate technical tradeoffs clearly to engineers, product managers, and senior stakeholders to align on priorities and outcomes.
• Maintain deep technical involvement in model training, serving, debugging, and optimization workflows while leading a team of engineers.
• Ensure efficiency improvements are not one-off fixes but are embedded into reusable systems and guardrails that benefit the broader Ads ML ecosystem.

🎯 Requirements

• Deep ML Engineering Experience: Proven hands-on experience with model training, serving, debugging, and optimization.
• Hands-on Optimization Background: Direct experience improving training loops, serving systems, profiling workflows, model/inference efficiency, or GPU utilization.
• Strong Managerial Ability: Experience building, coaching, and leading engineering teams, managing delivery, and making prioritization tradeoffs under ambiguity.
• Distributed Systems Fluency: Proven ability to reason about production-scale ML systems and the tradeoffs governing reliability, speed, cost, and scale.
• Customer and Platform Instincts: Ability to serve modeling teams while building reusable, platform-level systems rather than only heroic one-offs.
• Strong Communication: Ability to clearly explain technical tradeoffs to engineers, PMs, and senior stakeholders.

🏖️ 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

Skills & Technologies

PyTorch

Remote

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Reddit Inc.

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About Reddit Inc.

Reddit is a social media platform where users submit, vote, and comment on content organized into topic-based communities called subreddits. Founded in 2005, it offers forums for news, hobbies, advice, and discussion, enabling real-time conversations and content ranking through upvotes and downvotes. With millions of daily active users globally, Reddit hosts diverse communities moderated by volunteers, supports multimedia posts, and provides advertising and premium membership options. The platform emphasizes user anonymity, community governance, and crowdsourced information, making it a hub for niche interests, viral content, and public discourse. Reddit went public in 2024 and is headquartered in San Francisco.

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