Currently tracking 53 active AI roles, down 44% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $131k–$1500k (avg $606k).
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.
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 |
|---|---|---|
| Creative Tech Researcher 4 Machine Learning Researcher to define the next generation of creative technology by integrating groundbreaking research into Netflix's production ecosystem, focusing on foundational video generation models, data strategy, large-scale training, and evaluation frameworks. | Post-trainData | 9 |
| Research Scientist 5 — Content Representation Models (CRM) Research Scientist role focused on developing foundation models for content understanding at Netflix, leveraging embeddings and representation learning to enhance personalization. The role involves applied research, implementing new approaches, and contributing to production models with a tight research-to-impact loop. | Pretrain |
| 9 |
| Machine Learning Scientist (L4/L5) - Multi-modal Algorithms for Games Machine Learning Scientist role focused on research and development of LLMs, VLMs, and multi-modal foundations for games, with a strong emphasis on inference efficiency, model optimization (distillation, pruning), and generative visuals. The role involves fine-tuning, alignment, and integrating models for real-time interaction and cost-effectiveness. | Post-trainServe | 9 |
| Research Scientist 5 - Content Promotion and Distribution Research Scientist at Netflix focused on developing and deploying AI/ML solutions for content promotion and discovery. The role involves end-to-end development, including model training, evaluation, and productization of vision-language and multimodal LLM systems, with a focus on advancing the state of the art and enhancing member experience. | Post-trainServe | 9 |
| Research Scientist 4 - Machine Learning and Inference Research, LLM Post-Training Research Scientist 4 at Netflix focused on post-training LLMs, particularly using RL techniques, and potentially other areas like reasoning, alignment, distillation, tool use, memory, and calibration. The role involves fundamental research, publishing at top venues, and translating research into impact at scale within the consumer domain. | Post-train | 9 |
| Research Scientist 5/6 – AI for Member Systems Research Scientist role at Netflix focused on applied AI/ML for member systems, including personalization, recommendations, and search. The role involves driving applied research, conceptualizing and implementing algorithmic solutions, and developing production-ready systems using state-of-the-art techniques like LLM pretraining and fine-tuning. | Post-trainPretrain | 9 |
| AI/ML Scientist Intern, AIMS AI Foundations (PhD) – Fall 2026 PhD intern role focusing on research and engineering foundations for next-generation member experiences, spanning agentic AI, LLM evaluation, multimodal modeling, and training data curation. The role involves designing and running experiments, building prototypes, and contributing to team projects. | AgentPost-train | 8 |
| Research Engineer 5 - LLM-Driven Product Understanding Research Engineer to research, develop, and iterate on LLM prototypes for member understanding, focusing on evaluation and simulation systems. The role involves driving roadmap, influencing product direction, and collaborating across teams, requiring strong deep learning, LLM, and software engineering skills. | Eval GatePost-train | 8 |
| Research Engineer 4/5 – AI for Member Systems Research Engineer at Netflix to apply ML expertise to design, develop, and scale personalization systems and algorithms for member experiences. This role involves creating production-ready ML solutions, optimizing models, and conducting experiments to improve key business metrics. | ShipPost-train | 8 |
| Research Scientist 5, Signal Privacy - Ads DSE Research Scientist role focused on signal privacy for Netflix's ad-supported tier, involving machine learning, statistical modeling, and data analysis with a strong emphasis on privacy-enhancing technologies in ad targeting, ranking, and optimization. | Post-train | 7 |
| Video Algorithms Intern, Video Coding (Gaussian Splatting), Fall 2026 Internship role focused on exploring and improving Gaussian Splatting (GS) for future streaming formats, involving research into model compression, training time reduction, and rendering efficiency. | Data | 7 |
| Machine Learning Scientist (L4) - Content & Conversation Modeling Machine Learning Scientist to develop, optimize, and deploy scalable ML solutions for content strategy, acquisition, scheduling, and advertising at Netflix. The role involves end-to-end ML model development, from ideation to deployment and monitoring, with a focus on informing content decisions and partnering with cross-functional teams. | Ship | 7 |
