Senior Machine Learning Engineer, ML Efficiency

Reddit Reddit · Consumer · United States · Remote · Ads Engineering

Senior Machine Learning Engineer focused on improving the efficiency (speed, cost, scalability) of ML model training and inference for Reddit's Ads ML workloads. This role involves diagnosing bottlenecks, building performance tooling, and collaborating with model owners and platform teams to implement optimizations.

What you'd actually do

  1. Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads.
  2. Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging.
  3. Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time.
  4. Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency and cost visibility, and operational confidence for heavy models.
  5. Work with model owners and platform teams to land pragmatic fixes while helping the team gradually standardize repeated solutions.

Skills

Required

  • ML systems experience
  • Production ML workloads
  • Training efficiency improvement
  • Serving efficiency improvement
  • Technical judgment
  • Project ownership
  • Collaboration

Nice to have

  • GPU training
  • GPU serving
  • PyTorch
  • Distributed training frameworks
  • Kernel optimization
  • Runtime optimization
  • Launch certification systems
  • Efficiency benchmarking systems
  • Cost observability systems
  • Model compression
  • Quantization
  • Pruning
  • Distillation
  • Checkpoint optimization

What the JD emphasized

  • Deep ML systems experience close to real production models and workloads
  • 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
  • Ability to own complex projects end to end and collaborate effectively across team boundaries

Other signals

  • ML Efficiency
  • Training Systems
  • Inference and Serving Paths
  • Optimization