# Machine Learning Engineer 5 - Decisioning & Optimization at Netflix

- Company: Netflix
- What the company does: Watch Netflix movies & TV shows online or stream right to your smart TV, game console, PC, Mac, mobile, tablet and more. Backed by IVP and Redpoint.
- Company website: https://www.netflix.com
- Type: Startups (AI role)
- Level: Mid level
- Location: 4 Locations
- Work setup: On-site
- Posted: 2026-09-02
- Apply by: 2026-10-17
- Apply: https://netflix.wd108.myworkdayjobs.com/en-US/Netflix/job/New-York/Machine-Learning-Engineer-5---Decisioning---Optimization_JR33085
- Page: https://www.1752.vc/careers/jobs/netflix-machine-learning-engineer-5-decisioning-and-optimization/

## About the role

Build and operate end-to-end ML model serving infrastructure for real-time ad decisioning: model publishing, packaging, validation, deployment into the serving stack with zero-downtime hot-swap Scale the inference path to support dozens of concurrent models on every ad request at 1M+ QPS with strict latency budgets, including batching strategies, CPU/GPU allocation, model versioning, and fallback tiers

## What they're looking for

- 7+ years of software engineering experience, 3+ years focused on ML infrastructure, model serving, or ML platform work in an ads or real-time decisioning context
- Built and operated real-time model serving systems at high QPS with sub-20ms latency: online inference, feature stores, model registries, model hot-swap, canary and shadow rollout
- Proficiency in Java, Python, or Scala with a solid understanding of multi-threading, memory management, and performance optimization for latency-critical paths
- Hands-on with ML serving frameworks: serialization, runtime optimization, and deployment constraints
- Experience with feature engineering pipelines for real-time systems: online/offline consistency, hydration strategies, caching, and freshness tradeoffs
- Strong understanding of model monitoring in production: drift detection, prediction distribution analysis, calibration, and latency profiling