Currently tracking 50 active AI roles, with 144 new openings in the last 4 weeks. Primary focus: Ship · Engineering. Salary range $131k–$1500k (avg $604k).
| Title | Stage | AI score |
|---|---|---|
| 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 |
| 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 |
| Senior Manager, Content Promotion & Distribution Data Engineering Senior Manager to lead a Data Engineering team focused on building and operating multi-modal data foundations (text, metadata, image, video, audio) for ML and GenAI model development and evaluation within Netflix's Content Promotion & Distribution ecosystem. The role involves managing a heterogeneous team of Data, Software, and ML Engineers, partnering with cross-functional leaders, and providing technical vision for data products supporting analytics, experimentation, and ML at scale. | Data | 5 |
| Software Engineer L4/L5 - Data and Feature Infrastructure, Machine Learning Platform Netflix is seeking a Software Engineer to build and scale a next-generation ML data and feature platform. This role will focus on creating infrastructure for defining, computing, storing, and serving ML features and labels, enabling ML practitioners to improve productivity and foster innovation across various domains like personalization, payments, and ads. The platform will support both high-throughput training and low-latency inference use cases, including a centralized feature and embedding store for sharing. | DataServe | 5 |
