Engineering Manager, Ads ML Efficiency

EngineersOfAI; United States; Hybrid; USD 150,000 - 200,000; Full time

Job summary

EngineersOfAI is seeking an Engineering Manager to lead a dedicated Ads ML Efficiency team. The role emphasizes model optimization and systems performance, collaborating closely with various teams to achieve measurable efficiency and scalability in ML processes.

The ideal candidate possesses deep ML engineering experience, a strong background in optimization, and significant managerial abilities to mentor a high-performing team. Remote work options are available.

Qualifications

• Extensive knowledge of model training, serving, and debugging.
• Experience optimizing training loops and GPU utilization.
• Leadership experience in managing delivery and teams.

Responsibilities

• Lead a team of ML engineers focused on optimization.
• Define roadmaps for training and inference optimization.
• Drive reductions in model training time and online latency.
• Guide development of performance tooling and systems.
• Partner with teams to accelerate high-priority launches.

Skills

ML Engineering Experience

Optimization Background

Managerial Ability

Distributed Systems Fluency

Job description

Reddit offers flexible work options. If you live near one of our physical office locations, our doors are open for you to come into the office as often as you'd like. If you do not live near an office, you may apply to work remotely in any country where we have a physical presence.

About the Role

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.

This role sits at the intersection of ML modeling, systems optimization, and organizational leverage. You will partner closely with ranking teams, ML Platform teams, and serving owners to identify the highest-value bottlenecks, land measurable efficiency wins, and build the tooling and operating mechanisms that make those wins repeatable.

What you’ll do:

• 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.

What we’re looking for:

• 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.