Senior Machine Learning Engineer, ML Efficiency - name

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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. This person will be a key senior engineer on that team, owning meaningful efficiency work across training systems, inference and serving paths, launch-readiness tooling, and reusable optimization capabilities for Ads ML.

This role sits at the intersection of ML modeling, systems optimization, and engineering leverage. The engineer will partner closely with ranking teams, serving owners, and ML Platform to identify important bottlenecks, land measurable efficiency wins, and help build the mechanisms that make those wins repeatable.

What you’ll do

• Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads.
• Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging.
• Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time.
• Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency and cost visibility, and operational confidence for heavy models.
• Work with model owners and platform teams to land pragmatic fixes while helping the team gradually standardize repeated solutions.
• Contribute to the team’s technical direction by surfacing patterns, tradeoffs, and opportunities for reuse or automation.
• Mentor less-experienced engineers through code, debugging, measurement rigor, and strong execution habits.

What we’re looking for

• Deep ML systems experience close to real production models and workloads, not just generic infra exposure.
• Direct hands-on experience improving training or serving efficiency with measurable outcomes.
• Strong technical judgment across model-level, runtime-level, and infrastructure-level optimization choices.