[Remote] Engineering Manager, Ads ML Efficiency
Note: The job is a remote job and is open to candidates in USA. Reddit is a community of communities and is seeking an Engineering Manager for their Ads ML Efficiency team. This role will focus on leading a team to optimize model training and inference processes, enhancing efficiency and scalability in 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
Skills
- 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
- 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
Company Overview
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