AI Research Scientist 4 - Generative Models, Recommender Systems

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

The Promotional Assets & Creative Technology AIMS team is at the forefront of Netflix's efforts to effectively promote titles and enhance the content discovery experience. We are looking for a seasoned Machine Learning Scientist to design and develop innovative Machine Learning (ML) solutions. In this rare opportunity, you will have the chance to define the direction for algorithmic strategies and spearhead the execution of ML solutions and ultimately shape how titles are promoted on Netflix and discovered by our members.

• Track advancements in LLM and generative model research, and translate them into concrete opportunities for the problem space we are working on.

• Apply state-of-the-art techniques to high-impact problems such as summarization quality and building LLM-based evaluators ("judges") that assess whether generated content meets quality bars.

• Lead end-to-end ML development on these problems: research, model training, and evaluation.

• Optimize generated content for member impact, treating content generation and audience targeting as coupled problems — drawing on recommendation systems thinking to ensure generated assets are optimized for the members they're meant to reach.

• Partner with ML scientists and engineers to integrate models into business applications and platforms.

• Translate complex technical concepts for both technical and non-technical stakeholders to inform strategic decisions.

• Act as a subject matter expert, working cross-functionally to identify high-impact opportunities and define and execute roadmaps.

• Engage with the ML community, internal and external, to learn, to teach, and to contribute to building a great Netflix brand.

• Demonstrated habit of tracking the LLM/generative model literature and a knack for spotting which advances are relevant to real business problems.

• Strong foundation in ML and Deep Learning, especially in Natural Language Processing / Understanding and Generation. Research experience in summarization or generated-text evaluation (e.g., LLM-as-judge methods) is a big plus; background in vision models such as diffusion models is a plus.

• Expertise in one or more LLM post-training approaches.

• Experience with recommendation systems, personalization, or other member-facing optimization systems is a strong plus.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Job is open for no less than 7 days and will be removed when the position is filled.