Senior Machine Learning Engineer (ML Efficiency)

About the role

Join Reddit as a Senior Machine Learning Engineer (ML Efficiency) and be a key player in building a dedicated Ads ML Efficiency function. You will own meaningful efficiency work across training systems, inference and serving paths, launch-readiness tooling, and reusable optimization capabilities for Ads ML. This role requires strong technical judgment, deep ML systems experience, and the ability to collaborate effectively across team boundaries. Key missions: 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. Profile: - Direct hands-on experience improving training or serving efficiency with measurable outcomes - Ability to own complex projects end to end and collaborate effectively across team boundaries - Good customer and platform instincts: can solve concrete bottlenecks while keeping maintainability, adoption, and future reuse in mind - Strong communication: able to explain tradeoffs clearly to engineers and partner teams - Strong technical judgment across model-level, runtime-level, and infrastructure-level optimization choices - Deep ML systems experience close to real production models and workloads, not just generic infra exposure - Experience with GPU training or serving migrations - Experience with PyTorch, distributed training frameworks, or kernel/runtime optimization - Experience building launch certification, efficiency benchmarking, or cost observability systems - Experience in organizations where platform and applied modeling responsibilities are split across multiple teams - Experience with model compression or deployment optimizations such as quantization, pruning, distillation, or checkpoint optimization