Netflix has 86 active AI-related job listings. The majority of these roles are focused on agents, comprising 34% of the total, and application development, at 33%. Engineering is the primary function for these positions. The company is actively hiring for roles involving model serving, fine-tuning, and recommender systems. Over the last 30 days, Netflix has added 22 new AI roles, representing a 16% increase compared to the previous 30-day period.
Currently tracking 53 active AI roles, down 44% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $131k–$1500k (avg $606k).
Netflix currently has 80 active AI-related roles in our index. The most common open titles are: AI Engineer 6 - AI Foundation & Tooling, Ads Platform, AI Product Manager, Content Platform Operations & Publishing, AI/ML Scientist Intern, AIMS AI Foundations (PhD) – Fall 2026, Analytics Engineer 5 - Ad Ranking, Art Director - Ink. Most positions are in Engineering and Research.
Netflix's active AI hiring is concentrated in: agents (36%), application (24%), serving infrastructure (14%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Netflix is hiring AI talent in: United States (76 roles), Poland (4 roles), Canada (3 roles).
Job postings at Netflix most frequently mention: Machine Learning, Ads & Ranking ML, Production ML Systems, Generative AI, Data Science.
In the past 30 days, Netflix has posted 12 new AI-related roles. That is a -33% change versus the prior 30 days (18 → 12).
| Title | Stage | AI score |
|---|---|---|
| ML Software Engineer 6 - AI for Member Systems (AIMS) Netflix is seeking an ML Software Engineer to build and operate the platform integration layer for their AI for Member Systems (AIMS) organization. This role focuses on connecting AIMS' AI capabilities (recommendations, personalization, search, discovery) to Netflix's ML infrastructure and serving ecosystem, ensuring scalability and integration. The engineer will drive architectural decisions, own integration points, and design reusable infrastructure components to support ML models and GenAI capabilities across various surfaces. | ServeAgent | 8 |
| Machine Learning Engineer 5 - Decisioning & Optimization Netflix is seeking an ML Engineer to build and operate real-time ML model serving infrastructure for their ad tech ecosystem. The role focuses on scaling inference paths to support high QPS with strict latency budgets, optimizing feature serving, productionizing scoring and ranking models, and building model performance monitoring. Experience with high-QPS, low-latency real-time model serving systems and operating at scale is critical. |
| ServeAgent |
| 8 |
| Inference Specialist, Creative Technology - InterPositive This role focuses on operating and supporting custom generative AI inference workflows for creative projects at Netflix. The specialist will run, monitor, and troubleshoot GPU-based inference jobs, prepare and validate inputs, tune inference parameters, debug generation issues, and maintain repeatable launch workflows. They will partner with researchers and engineers to test new models and translate experimental capabilities into production practices, ultimately owning quality control for generated outputs and bridging communication between creative, production, research, and engineering teams. | ServePost-train | 8 |
| Machine Learning Engineer 5 - Globalization Machine Learning Engineer at Netflix focused on optimizing training and inference efficiency for LLMs and Multimodal LLMs within the Globalization team. The role involves designing and building scalable systems, optimizing data pipelines, distributed training, mixed precision, KV cache, batching, and quantization to improve performance, latency, and reliability of ML models for Netflix's global catalog. | ServePost-train | 8 |
| Distributed Systems Engineer 6 - Decisioning & Optimization Netflix is seeking a Distributed Systems Engineer 6 to lead the technical direction of the Decisioning & Optimization team within their ad tech ecosystem. This role involves architecting and scaling real-time ad decisioning systems, including ML model serving infrastructure, ranking, scoring, and optimization under strict latency constraints. The engineer will also drive operational excellence and collaborate with Science and Platform teams to productionize ML algorithms. | ServeAgent | 7 |
| Machine Learning Engineer 5 - Ads Inventory Management & Forecasting Machine Learning Engineer at Netflix focused on Ads Inventory Management & Forecasting. The role involves building end-to-end ML model deployment and inference infrastructure for low-latency real-time ad systems, handling large data volumes with Spark, and productionizing predictive models for campaign effectiveness forecasting. It also includes building scalable simulation solutions for inventory scenarios and collaborating with cross-functional teams. | ServeData | 7 |
| Machine Learning Engineer 5 - Ads Platform Engineering Netflix is hiring Machine Learning Engineers for their Ads Platform Engineering teams. The role involves building and deploying ML models for low-latency real-time ad systems, focusing on areas like inventory forecasting, ad serving, programmatic interfaces, member experience, and audience targeting. Responsibilities include developing and productionizing predictive models for campaign effectiveness, yield optimization, bid ranking, and dynamic allocation, as well as building scalable simulation solutions. Experience with big data tools like Spark and proficiency in languages like Java, C++, Python, or Scala are required. | ServeAgent | 7 |
| Software Engineer L4/L5, Model Serving Systems, Machine Learning Platform Netflix is seeking a Software Engineer for their Machine Learning Platform team to develop and expand their model serving systems, focusing on infrastructure for LLMs and other large foundation models. The role involves building scalable, robust systems for online ML model inference, optimizing for latency and cost, and ensuring high availability and performance. This is a highly cross-functional role partnering with various engineering and data science teams. | ServeAgent | 7 |
