Machine Learning Engineer (L5) - Studio Media Algorithms

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

The Studio Media Algorithms team is at the forefront of innovation to enhance and support the vision of creators of movies, TV shows, and other modes of entertainment. This team's work is responsible for increasing member value and driving efficiency of the content creation process, ultimately creating more joy for viewers all over the world.

We are building a next-generation Media Search system for Netflix Studios to help search media during the Production and Post-Production process of filmmaking. In this role, you will play a critical part in building and deploying core pieces of this algorithm stack. We are looking for a seasoned machine learning (ML) engineer with hands-on experience building a media search stack, with skills including but not limited to - Large Language Models (LLMs), Vision Language Models (VLMs), and Computer Vision (CV).

In this role, you will:

• Design and develop machine learning models that enhance media search capabilities, focusing on integrating Large Language Models (LLMs) and Vision Language Models (VLMs).

• Develop innovative algorithms for media indexing and retrieval, leveraging the latest advancements in LLMs and CV to improve search accuracy and efficiency.

• Collaborate with interdisciplinary teams, including data scientists, engineers, product managers, and creative professionals, to align machine learning solutions with studio needs and creative goals.

• Drive the deployment and optimization of ML models in production environments, ensuring robust performance and scalability.

• Inform and advocate for the building of right infrastructure pieces needed to scale media search systems. 

About you:

• Extensive experience in machine learning engineering, with a strong focus on developing and deploying ML models in production settings.

• Deep understanding of LLMs, VLMs, Multimodal-ML, and their application in search and retrieval systems.

• Proficient in programming languages such as Python, with experience in ML frameworks like PyTorch.

• Excellent communication and collaboration skills, with the ability to work effectively in a multidisciplinary environment.

• Experience with cloud-based ML deployment and large-scale data processing systems.

Bonus experience:

• Experience building Agentic AI workflows.

• Familiarity with the content creation process, including media production and post-production workflows.

• Working knowledge of media-production adjacent tools such as AVID, Davinci-Resolve, etc.

Our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $150,000 - $750,000.

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.