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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer 5 - Decisioning & Optimization - **Company:** Netflix, Inc. - **Location:** New York, NY, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Java (Programming Language), A/B Testing, Serialization, Memory Management, Java Virtual Machine (JVM), Python (Programming Language), Machine Learning, Performance Tuning, Azure Machine Learning, Software Engineering, Multithreading, Real Time Systems, Feature Engineering, Yield Optimization, Machine Learning Operations - **Published:** June 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9b7f1fbe13ea4d31 ## About the Role Do you have experience in System performance optimization?, * 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 * Comfortable working at the boundary between ML research and production engineering: can take a model artifact and turn it into a production-ready service that meets SLA * Demonstrated ability to operate in an environment that requires both big-tech scale and startup speed Nice to Haves * Ads domain experience: ranking models, bid scoring, reserve pricing, yield optimization, dynamic allocation across guaranteed and non-guaranteed inventory * Experience with auction mechanics: multi-stage ranking, bid shading, bid prediction, marketplace competition dynamics * Built or improved budget pacing and delivery control systems * Built simulation or counterfactual testing platforms for marketplace or auction systems * Experience with A/B testing infrastructure for model rollouts: online experiments, holdout groups, interference-aware evaluation in marketplace settings * Familiar with CTV constraints: server-side ad insertion, live event ad serving at scale, burst traffic patterns * JVM ecosystem ## Description The Decisioning & Optimization engineering team owns the systems that determine which ad wins every impression, at what price, and how campaign budgets deliver across all inventory surfaces. Our work spans three platform areas: * ML infrastructure for model serving : real-time inference at 1M+ QPS, multi-model parallel evaluation, feature hydration, model lifecycle from canary deployment through production monitoring * Auction, ranking, and scoring : multi-stage candidate selection, scoring, bid valuation, dynamic pricing, and podding * Budget, pacing, and bidding : control systems for delivery optimization, budget planning, and bid computation We are scaling from a handful of production models to 10+ while maintaining sub-20ms P99 inference budgets. We are looking for an ML engineer who can build and operate the serving infrastructure these models run on, and who understands the ads decisioning context well enough to make the right engineering tradeoffs. What You'll Do * 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 * Design and optimize the feature serving path: feature hydration from Chronon, Signal Service, and real-time streams with sub-10ms P99 fetch latency and online/offline consistency * Productionize scoring and ranking models for multi-stage ad selection (retrieval, early ranking, full scoring) and integrate model outputs into auction * Build model performance monitoring in production: inference latency, prediction distribution shifts, feature drift detection, score calibration, and regression detection before revenue impact * Partner closely with Data Science & Platform teams * Build simulation infrastructure to replay production traffic against candidate models offline, enabling validation of marketplace changes before live rollout * Drive operational excellence for ML systems: reliability, observability, capacity planning, incident response, and scaling for live events with 35M+ concurrent viewers ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Boosting OpenSearch Performance: gRPC Search in Action](https://www.wearedevelopers.com/videos/1935-boosting-opensearch-performance-grpc-search-in-action) - [Asynchronicity and multithreading in C#](https://www.wearedevelopers.com/videos/447-asynchronicity-and-multithreading-in-c) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Beyond Autocomplete: Local AI Code Completion Demystified](https://www.wearedevelopers.com/videos/961-beyond-autocomplete-local-ai-code-completion-demystified) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)