āØ AI Insights & Summary
This Engineering Manager role at Reddit offers a unique opportunity to lead a groundbreaking Ads ML Efficiency function, directly impacting the speed, cost, and scalability of machine learning model training and inference. You'll be at the forefront of optimizing complex systems at a massive scale, leveraging your deep ML engineering and systems optimization expertise. This position is ideal for a leader passionate about building high-performing teams and driving measurable improvements in a globally recognized tech company known for its vibrant community and flexible work environment.
About the Company
Reddit is a community of communities, built on shared interests, passion, and trust. It's home to the most open and authentic conversations on the internet, with hundreds of thousands of active communities and millions of daily active unique visitors. Reddit is committed to a flexible workforce, allowing remote work in countries where they have a physical presence, or offering office-based work near their physical locations.
About the Role
Reddit is establishing a dedicated Ads ML Efficiency function to significantly enhance the speed, cost-effectiveness, safety, and scalability of model training and inference. 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. This role is at the intersection of ML modeling, systems optimization, and organizational leverage, partnering closely with ranking, ML Platform, and serving teams to identify critical bottlenecks, achieve measurable efficiency gains, and build repeatable tooling and operating mechanisms.
What You'll Do
- Lead & Grow: Hire, mentor, and retain a high-performing team of ML engineers and systems-oriented engineers focused 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 costs, and infra-driven launch risks.
- 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 production bottlenecks.
- Shape Team Evolution: Balance near-term "white-glove" optimization work with medium-term platformization and automation.
- Build XFN Alignment: Collaborate 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.
What We're Looking For
- Deep ML Engineering Experience: Close proximity to models, with in-depth understanding of 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: Proven experience building and leading teams, coaching engineers, managing delivery, and making prioritization trade-offs under ambiguity.
- Distributed Systems Fluency: Ability to reason about production-scale ML systems and the trade-offs governing reliability, speed, cost, and scale.
- Customer and Platform Instincts: Ability to act as a service provider to modeling teams while building reusable systems.
- Strong Communication: Clear articulation of technical trade-offs to engineers, PMs, and senior stakeholders.
- Ads Experience (Preferred): Experience in ads ranking, recommender systems, marketplace ML, or adjacent production ML domains.
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 with split ML platform and applied modeling responsibilities.
Benefits
- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs (workspace, professional development, caregiving support)
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
Pay Transparency
- The base salary range for this position is $230,000 - $322,000 USD.
- This role is eligible for equity and potentially a commission.
- Additional benefits include medical, dental, and vision insurance, 401(k) with employer match, generous time off, and parental leave.