Machine Learning Engineer (L5) - Content & Media ML Foundations
USA - Remote, United States
Netflix is one of the world's leading entertainment services, with 283 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.
To meet our members’ entertainment needs, Netflix has invested in scaled Content Production & Promotion workflows. With assistance from creative supervision and member feedback data, we have built tools & processes that use technologies like Computer Vision, Graphics, Machine Learning & Generative Algorithms to enable our creators to tell the best version of the story they want while allowing our studio to scale further.
The Content & Media ML Foundations team within Data Science and Engineering, builds and delivers high-leverage foundational ML solutions using Netflix’s unique media data, driving impact across content creation and promotion, member personalization and advertising (Ads). Our work advances multi-modal content understanding, and we conduct exploratory research on generative models to drive long-term innovations in filmmaking and media intelligence.
We are looking for an experienced ML engineer to develop, optimize and deploy scalable ML solutions that power content intelligence at Netflix.
Responsibilities
Design, build and optimize ML pipelines for content understanding and media intelligence.
Develop and deploy large-scale ML models efficiently using Netflix’s ML infrastructure.
Optimize model performance and inference efficiency, ensuring scalability in high-throughput distributed environments.
Automate ML workflows for training, tuning and deployment, enabling faster experimentation and productization.
Collaborate with ML scientists and engineers to integrate multimodal models and embeddings into downstream applications, including retrieval, ranking and search pipelines.
Improve ML observability, model evaluations, model monitoring, and debugging tools to ensure reliability of deployed models.
Stay up to date with ML infrastructure advancements, identifying new technologies and best practices to enhance efficiency.
About You
You have a strong foundation in machine learning and deep learning, including embedding methods, supervised and unsupervised learning, and deep learning architectures.
You have a track record of deploying ML systems at scale, particularly in high-performance inference and distributed training environments.
You have hands-on experience with training large generative models across multiple modalities (text, images, videos).
You have a strong understanding of feature engineering, data pipelines, and model lifecycle management.
You hold an advanced degree (MS or PhD) in Computer Science, Electrical Engineering, or a related technical field with a focus on machine learning, artificial intelligence or computer vision.
You have at least 5 years of relevant industry experience designing and implementing ML models, particularly in the areas of natural language processing, audio and video understanding.
You are proficient in Python and have experience with ML/DL frameworks such as PyTorch, or Jax.
You excel at complex problem solving with innovative solutions, developing novel algorithms, and adapting existing methods from literature to new challenges.
You are an excellent communicator, capable of explaining complex technical details to both technical and non-technical audiences.
You thrive in fast-paced dynamic environments, contributing positively to team collaboration and company culture.
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.
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 detail about our Benefits 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.
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Remote
Benefits/PerksAnnual salary Comprehensive benefits Disability Programs Family-forming Benefits Flexible Spending Flexible Spending Accounts Flexible time off Flexible work environment Health plans Health savings Mental health support Paid leave Paid leave of absence Paid Time Off Retirement plan with employer match Stock options
Tasks- Automate ML workflows
- Build
- Collaborate with ml scientists
- Collaboration
- Deploy
- Design
- Design and optimize ml pipelines
- Develop
- Develop and deploy large-scale ml models
- Hiring
- Improve ml observability
- Management
- Problem solving
Algorithms Artificial Intelligence Audio understanding Collaboration Computer Vision Content Creation Content Understanding Data Pipelines Data Science Debugging Debugging Tools Deep Learning Deep learning architectures Design Distributed Training Embedding methods Engineering Excel Experimentation Feature Engineering Generative models Infrastructure Jax Machine Learning ML ML Infrastructure Ml observability Model evaluations Model Lifecycle Management Model monitoring Monitoring Natural Language Processing New technologies Observability Personalization Python PyTorch Reliability Scalability Supervised Learning Unsupervised Learning Video understanding
Experience5 years
EducationAdvanced degree Computer Science Data Science Electrical Engineering Engineering MS Ph.D. Related technical field Technical field
TimezonesAmerica/Anchorage America/Chicago America/Denver America/Los_Angeles America/New_York Pacific/Honolulu UTC-10 UTC-5 UTC-6 UTC-7 UTC-8 UTC-9