Research Scientist 5 at Netflix focused on Machine Learning and Inference Research. The role involves tackling fundamental research questions, publishing in relevant venues, and translating research into tangible business impact for Netflix's consumer-facing products. The position requires a Ph.D. and experience in applying research to real-world problems, with a strong publication record and demonstrated technical expertise.

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.

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.

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.

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.

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.

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.

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.

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.

Machine Learning Scientist role focused on pricing science within a B2C subscription business. The role involves designing and implementing causal inference approaches and ML models to understand the impact of pricing actions on member behavior, conducting elasticity and willingness-to-pay research, and evolving measurement tools. The scientist will partner with Finance & Strategy and Product teams to translate findings into actionable business recommendations and own work from ideation to production systems.

Research Scientist role focused on identity modeling for Netflix's ad-tier members, involving machine learning, statistics, and data analysis techniques. The role requires experience in identity graphs, privacy, differential privacy, and anonymization to improve advertiser performance and member experience. Responsibilities include applying ML to business problems, identifying research opportunities, diagnosing data/model assumptions, delivering documented outputs, and serving as a strategic partner to stakeholders.

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.