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 |
|---|---|---|
| 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 |
| Software Engineer 5 – Agent Platform, AI Platform Software Engineer 5 role focused on building and operating the Agent Platform infrastructure at Netflix, including the Agent SDK, MCP Gateway, and evaluation stack. The role involves enabling other engineers to build, deploy, and run production-grade AI agents, with a strong emphasis on the full agent lifecycle (plan/act/observe, tool integration, deployment, operations) and robust evaluation mechanisms. | Agent |
| 9 |
| Data Scientist 5 - AI Evals Netflix is seeking an experienced Senior Data Scientist specialized in AI Evals to architect systems and frameworks for measuring, validating, and optimizing GenAI systems in production for both player-facing games and internal agentic tools. The role involves building evaluation pipelines, curating datasets, designing experiments to link technical attributes with user experience, and guiding evaluations for agentic systems. | Eval GateAgent | 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. | PretrainPost-train | 9 |
| Senior Machine Learning Manager, Merchandising and Content Understanding Senior Manager leading a team of Research Scientists, ML Engineers, Data Scientists, and Analytics Engineers focused on Multimodal Large Language Models (MLLMs) and agentic systems for deep, complex content understanding. The role involves defining strategy, overseeing end-to-end initiatives, managing and growing a multidisciplinary organization, and establishing best practices for complex algorithmic and agentic systems at Netflix scale. | AgentPost-train | 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 |
| 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 |
| 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 |
| Product Manager - Netflix Artist Experience Product Manager at Netflix focused on integrating generative AI capabilities into a unified artist studio experience for creative tooling, specifically for image and video generation, modification, and editing. | ShipAgent | 8 |
| Machine Learning Scientist 5 - Ads Bidding Netflix is seeking a Machine Learning Scientist 5 for their Ads Bidding team to design and implement ML-driven bidding algorithms for ad performance optimization. This role involves building, training, and evaluating these algorithms on large-scale production data, developing evaluation frameworks, and partnering with product teams. The goal is to deliver highly relevant ad experiences and drive advertiser results within Netflix's ad tech ecosystem. | Ship | 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 |
| 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 |
| 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 |
| Senior ML Engineer, GenAI - Games Senior ML Engineer at Netflix Games focusing on integrating GenAI into game development, including gameplay mechanics, asset generation, NPC behaviors, and internal tools. The role involves optimizing model performance, bridging research and production, and mentoring junior engineers. Requires strong software engineering skills in C++/C# and experience with the ML lifecycle. | AgentServe | 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 |
| Senior Game Engineer, GenAI - Games Senior Game Engineer at Netflix focused on integrating Generative AI into gameplay systems to create dynamic and player-responsive experiences. The role involves working with game designers, implementing GenAI features, integrating them into core game systems, developing safety and quality guardrails, and using GenAI for rapid iteration and tool building. Requires strong game engineering experience and applied GenAI skills with LLMs or Diffusion models. | Agent | 8 |
| Technical Director, GenAI - Games Technical Director for GenAI in the Games organization at Netflix, responsible for driving the engineering strategy, leading teams, developing AI-native gameplay mechanics, and scaling core GenAI capabilities like model inference and fine-tuning pipelines for player experiences. | ShipServe | 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 |
| AI Engineer 6 - AI Foundation & Tooling, Ads Platform Staff AI Engineer to build AI infrastructure and agentic workflows for software development lifecycle at Netflix Ads Platform. This greenfield role focuses on applied AI, leveraging LLMs, agentic frameworks, and RAG to solve infrastructure and product problems, aiming for AI-native engineering. | Agent | 8 |
| Machine Learning Scientist (L6) - Live Ads Netflix is seeking a Machine Learning Scientist (L6) to serve as a vertical technical lead for their Live Ads ML problem areas, including forecasting, targeting, personalization, bidding, pacing, auction, and yield optimization. The role involves defining and driving the ML technical roadmap, architecting and delivering ML solutions, designing and implementing algorithms, building, training, and evaluating models on large-scale production data, and developing evaluation frameworks. The ideal candidate has 7+ years of experience building and shipping production ML systems, deep knowledge of ML and optimization, and experience in ad optimization. | Ship | 7 |
| Machine Learning Scientist 5 - Ads Forecasting Netflix is seeking a Machine Learning Scientist to build predictive models for their ad-supported tier. This role involves developing supervised ML models to forecast campaign delivery outcomes, replacing a simulation engine with learnable models. The scientist will own the end-to-end modeling process, from feature engineering to production deployment and monitoring, collaborating with ML engineers and cross-functional partners. The goal is to create accurate, robust, and explainable forecasts to power various aspects of the ad business. | Agent | 7 |
| Machine Learning Scientist 5 - Sales, Ads DSE Machine Learning Scientist role focused on building AI agents and automation systems to improve sales productivity and revenue growth within Netflix Ads. The role involves defining the ML roadmap, designing and deploying production ML models and AI agents end-to-end, and partnering with analytics engineers and stakeholders. | Agent | 7 |
| Storyboard Artist - Ink Netflix is seeking a Storyboard Artist to join their Inkubator team, focusing on developing animated shorts and specials using experimental GenAI-native production pipelines. The role involves creating high-quality storyboards, exploring hybrid workflows with traditional and new tools, and contributing to feature-quality content. The artist will collaborate with creative leadership, translate scripts into visuals, brainstorm story elements, and drive look development by blending generative imagery with traditional methods. Building workflows, templates, and scripts is also a key responsibility, alongside ensuring timely delivery and fostering a collaborative environment. | Agent | 7 |
| Senior Manager, Critical Operations & Reliability Engineering Senior Manager of Site Reliability Engineering to lead infrastructure organizations at Netflix, focusing on setting reliability standards and leading the SRE team supporting the streaming architecture. The role involves managing an evolving infrastructure towards a millions-of-agents ecosystem, with AI agents embedded in incident management, capacity planning, and reliability posture. Requires experience in AIOps, anomaly detection, or agentic infrastructure systems, and driving technical adoption across complex organizations. | AgentEval Gate | 7 |
| Senior Applied AI Lead, Talent Senior Applied AI Lead for Netflix's Talent team, focusing on identifying, prototyping, and shipping AI-powered capabilities across the full Talent lifecycle. The role bridges Talent domain expertise with AI fluency, building working prototypes and translating them into production partnerships with engineering. | Agent | 7 |
| Manager, Member Experience - Ads DSE Manager for Netflix's Member Experience team within Ads Data Science and Engineering. This role focuses on understanding and optimizing the impact of ad delivery policies on member experience using causal inference and predictive models. The team will build models and workflows to drive ad delivery optimization, balancing revenue, member experience, and advertiser outcomes. The role involves leading a team of ML Scientists, Data Scientists, and Analytics Engineers, fostering partnerships, and ensuring high-quality technical outputs. | Agent | 7 |
| Machine Learning Scientist 5, Markeplace - Ads DSE Machine Learning Scientist at Netflix focused on building and optimizing the ad tech ecosystem. This role involves designing and implementing ML algorithms for ad quality and performance, training and evaluating models on large-scale production data, and developing evaluation frameworks. The goal is to create data-driven solutions for relevant ad experiences and advertiser results, balancing member experience and revenue. | AgentEval Gate | 7 |
| 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 |
| Senior Product Designer, AI Enablement Senior Product Designer for AI Enablement at Netflix, focusing on architecting agentic platform experiences and intelligent infrastructure for the Ads Suite. This role involves defining scalable patterns for AI integration, operationalizing agentic workflows, and serving as a quality gatekeeper for AI outputs, bridging design and ML engineering. | Agent | 7 |
| Engineering Manager, Ads Signals & Targeting Engineering Manager for Netflix Ads, focusing on core targeting and signals platforms. This role involves leading teams to build and scale systems for audience and contextual targeting, enabling relevant and privacy-forward ad experiences. It requires partnering with ML teams on advanced models, working on high-scale, low-latency services, and shaping signal activation for targeting, optimization, and measurement. The role emphasizes ownership, rapid execution, and building high-performing teams in a fast-paced environment. | AgentData | 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 |
| 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 |
| 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 |
| Manager, Generative Workflows - Production Technology Manager role focused on translating GenAI experimentation into production-ready guidance for content production partners at Netflix. This involves consulting on feasibility and value, identifying where GenAI improves quality/efficiency/speed, and grounding the technology in creative and operational constraints. The role will prototype, deliver, optimize workflows, educate teams, and provide feedback to product and innovation teams. | Agent | 7 |
| Trust Services and Enforcement Engineering Manager Engineering Manager for Netflix's Trust Services and Enforcement team, focusing on building and scaling fraud mitigation services. This role involves applying Generative AI to enhance detection and counter evolving AI-driven attack vectors, while managing a team and driving cross-functional alignment on security risk posture. | Agent | 7 |
| Machine Learning Scientist 5 - Games Machine Learning Scientist 5 focused on forecasting and audience research within Netflix Games. The role involves building foundational ML building blocks like embeddings and models, accelerating product development through tools and pipelines, bridging the Netflix ecosystem, designing scalable ML pipelines, and establishing technical standards for ML application in game domains. Requires a PhD and significant experience in leading end-to-end ML projects and navigating large technical organizations. | Data | 7 |
| Machine Learning Scientist 5 - Ad Ranking Machine Learning Scientist role focused on ad ranking and personalization at Netflix, involving designing, implementing, and evaluating ML models and optimization algorithms on large-scale production data. The role requires strong collaboration with product teams and communication of technical results to stakeholders. | ShipEval Gate | 7 |
| Manager, Data Science & Engineering - Title & Launch Management Manager for a Data Science and Engineering team at Netflix, focusing on building automated systems and AI/agentic solutions for content launch and promotion. The role involves leading a multidisciplinary team, shaping strategy, and driving execution of end-to-end AI solutions. | Agent | 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 |
| 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 |
| AI Product Manager, Content Platform Operations & Publishing Product Manager role at Netflix focused on integrating AI/ML into content platform operations and publishing tools to create personalized experiences, automate asset creation, and optimize content distribution. Requires experience with the ML lifecycle, launching AI-enabled products, and evaluating ML techniques. | Ship | 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 |
| 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 |
| Software Engineer L4/L5 Training Platform, Machine Learning Platform Software Engineer on the Machine Learning Platform (MLP) team, responsible for designing and building the platform that powers large-scale machine learning model training, fine-tuning, model transformation, and evaluations workflows for the entire company. Focuses on optimizing systems and models for scale and cost-effectiveness, and designing user-friendly APIs for ML practitioners. | DataPost-train | 7 |
