Machine Learning Scientist (L4) - Content & Studio

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 seeking early-career talent with demonstrated academic or industry research experience in generative and reconstructive image and video models, such as Diffusion Models and Gaussian Splats, with applications in areas like improving the quality of video content.

In this role, you will:

• Design and develop machine learning models that enhance the quality of images and videos using techniques such as super-resolution, detail generation, and SDR-to-HDR conversion. 

• Stay on top of academic research in video-quality space, and find innovative ways to further the state-of-the-art while keeping the goals of the use-cases central to the research.

• 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 the right infrastructure pieces needed to scale media search systems.

About you:

• Demonstrated experience in Machine Learning research with a track record of academic publications, and a passion for furthering state-of-the-art research.

• Deep understanding of generative and reconstructive technologies such as Diffusion models, Gaussian Splatting, Super-Resolution, SDR to HDR, etc.

• A passion for image and video processing, computational photography, color-science, and adjacent fields.

• 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 - for example, Dockerization!

Bonus experience:

• Deeper knowledge of color science and ACES Color Standards.

• 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.