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 |
| Machine Learning Manager - Localization Algorithms Lead a team of Research Scientists and Machine Learning Engineers focused on multimodal LLM and audio algorithms for localization at Netflix. The role involves mentoring, strategic planning, and driving the development and productionization of cutting-edge ML systems to enhance global member experience. | Post-trainAgent |
| 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 |
| Software Engineer 5 – Model Runtime, AI Platform Software Engineer 5 on the Model Runtime team at Netflix, focusing on building and optimizing infrastructure for training, alignment (RLHF, DPO, PPO), and serving of ML models, including multimodal and diffusion models. The role involves deep systems programming, distributed training at scale, and performance tuning across the full stack, from PyTorch to GPU kernels. | Post-trainServe | 8 |
| Research Scientist (L5) - Content Understanding Research Scientist at Netflix focused on content understanding and merchandising. The role involves designing and developing models, conducting quality evaluations for algorithms, and applying ML/Gen AI to video, audio, and textual data. The goal is to improve content metadata and the discovery experience for members. | Post-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 |
| CG Artist, Experimental - INK Netflix is seeking CG Experimental Artists to join their pioneering team creating animated shorts and specials using experimental GenAI-native production pipelines. This role will support show-specific workflow and technical needs, shape storytelling and visual aesthetics, and develop new workflows and technologies. Responsibilities include using GenAI tools for concept generation, creating shot-ready assets, building and fine-tuning generative models, and testing/documenting generative tools and workflows. | Post-trainData | 7 |
| Technical Director - Inkubator Netflix is seeking Technical Directors for its Inkubator team to create animated shorts and specials using experimental, creative-led, GenAI-native production pipelines. The role involves supporting show-specific workflows, strategizing with artists to develop creative solutions, providing tool support, documenting training materials, and beta testing new tools. A key responsibility is identifying and performing custom fine-tuning for models based on show requirements, integrating both internal and external tooling. The role also involves monitoring artist pain points, troubleshooting data handoffs, evaluating new tool releases, identifying standardization opportunities, and collaborating on compute and workstation requirements. | Post-trainServe | 7 |
| Engineering Manager, Core Applications - AI for Member Systems Engineering Manager for Core Applications AI team at Netflix, focusing on building foundational AI capabilities for member systems, including reward models, entity libraries, LLM post-training frameworks, and utility optimization. The role involves leading a team, setting vision, designing APIs, and partnering with other teams to ensure adoption and integration of shared components. | Post-trainAgent | 7 |