# Engineering Manager, Ads ML Efficiency **Company**: Reddit **Location**: Remote - United States **Work arrangement**: remote **Experience**: senior **Job type**: full-time **Salary**: $230,000-$322,000 USD **Category**: Engineering **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/reddit/jobs/8022366?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_e3d05df3-5ad ## Description Reddit is building a dedicated Ads ML Efficiency function to make model training and inference materially faster, cheaper, safer, and more scalable. As the Engineering Manager for this team, you will lead a group focused on model optimization, training efficiency, GPU enablement, load testing, model performance tooling, and efficiency guardrails across Ads ML. ## Responsibilities - Lead & Grow: Hire, mentor, and retain a high-performing team of ML engineers / systems-oriented engineers working on model optimization and ML efficiency. - Set Technical Direction: Define the roadmap for training optimization, inference optimization, launch-readiness tooling, and reusable efficiency primitives across Ads ML. - Deliver Measurable Wins: Drive reductions in model training time, online latency, serving cost, and infra-driven launch risk. - Build Systems and Tooling: Guide the development of profiling, benchmarking, load testing, observability, cost analysis, debugging, and efficiency certification systems. - Operate in the Critical Path: Partner with model owners and platform teams to accelerate high-priority launches and remove bottlenecks from the path to production. - Shape the Team’s Evolution: Balance near-term white-glove optimization work with medium-term platformization and automation. - Build XFN Alignment: Work closely with MLP, AMP, Ranking, and serving teams to clarify boundaries, upstream generic wins, and keep Ads needs on track. - Raise the Bar: 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. ## 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 working in organizations where ML platform and applied modeling responsibilities are split across multiple teams. ## 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 ### Required - ML Engineering - model optimization - training efficiency - GPU enablement - load testing - model performance tooling ### Nice to have - GPU training and serving migrations - PyTorch - distributed training frameworks - kernel/performance optimization - efficiency benchmarking - launch certification frameworks --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/reddit/jobs/8022366?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)