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.Machine Learning/Artificial Intelligence powers innovation in all areas of the business, from helping members choose the right title for them through personalization, to better understanding our audience and our content slate, to optimizing our payment processing and other revenue-focused initiatives. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation.The OpportunityWe are looking for a driven Software Engineer to join the Training Platform team under our Machine Learning Platform (MLP) org. MLP’s charter is to maximize the business impact of all ML use cases at Netflix through highly reliable and flexible ML tooling and infrastructure that supports key product functions such as personalized recommendations, studio algorithms, virtual productions, growth intelligence, and content demand modeling among others.In this role you will get to: Design and build the platform that powers large-scale machine learning model training, fine-tuning, model transformation and evaluations workflows and use cases from the entire companyCo-design and optimize the systems and models to scale up and increase the cost-effectiveness of machine learning model trainingDesign easy-to-use APIs and interfaces for experienced ML practitioners, as well as non-experts to easy access the training platformMinimum Job QualificationsExperience in ML engineering on production systems dealing with training or inference of deep learning models.Proven track record of building and operating large-scale infrastructure for machine learning use casesExperience with cloud computing providers, preferably AWSComfortable with ambiguity and working across multiple layers of the tech stack to execute on both 0-to-1 and 1-to-100 projectsAdopt and promote best practices in operations, including observability, logging, reporting, and on-call processes to ensure engineering excellence.Excellent written and verbal communication skillsComfortable working in a team with peers and partners distributed across (US) geographies & time zones.Preferred QualificationsUnderstand modern and real-world Machine Learning model development workflows and experience partnering closely with ML modeling engineersFamiliarity with cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI, etc.)Experience with large-scale distributed training and different parallelism techniques for scaling up training, such as FSDP and tensor/pipeline parallelismExpertise in the area of Generative AI, specifically when it comes to training foundation models, fine tuning them, and distilling them to smaller modelsWhat do we offer?Netflix's culture is an integral part of our success, and we approach diversity and inclusion seriously and thoughtfully. We are an equal opportunity employer and celebrate diversity, recognizing that bringing together different perspectives and backgrounds helps build stronger teams. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.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 $100,000 - $464,000.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 details about our Benefits here.Netflix has 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.
Locations
USA, California, United States of America (Remote)
Salary
100,000 - 464,000 USD / yearly
Estimated Salary Rangehigh confidence
250,000 - 450,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
ML engineering on production systems dealing with training or inference of deep learning modelsintermediate
building and operating large-scale infrastructure for machine learning use casesintermediate
large-scale distributed training and different parallelism techniques for scaling up training, such as FSDP and tensor/pipeline parallelismintermediate
Generative AI, specifically when it comes to training foundation models, fine tuning them, and distilling them to smaller modelsintermediate
Required Qualifications
Experience in ML engineering on production systems dealing with training or inference of deep learning models. (experience)
Proven track record of building and operating large-scale infrastructure for machine learning use cases (experience)
Experience with cloud computing providers, preferably AWS (experience)
Comfortable with ambiguity and working across multiple layers of the tech stack to execute on both 0-to-1 and 1-to-100 projects (experience)
Adopt and promote best practices in operations, including observability, logging, reporting, and on-call processes to ensure engineering excellence. (experience)
Excellent written and verbal communication skills (experience)
Comfortable working in a team with peers and partners distributed across (US) geographies & time zones. (experience)
Preferred Qualifications
Understand modern and real-world Machine Learning model development workflows and experience partnering closely with ML modeling engineers (experience)
Experience with large-scale distributed training and different parallelism techniques for scaling up training, such as FSDP and tensor/pipeline parallelism (experience)
Expertise in the area of Generative AI, specifically when it comes to training foundation models, fine tuning them, and distilling them to smaller models (experience)
Responsibilities
Design and build the platform that powers large-scale machine learning model training, fine-tuning, model transformation and evaluations workflows and use cases from the entire company
Co-design and optimize the systems and models to scale up and increase the cost-effectiveness of machine learning model training
Design easy-to-use APIs and interfaces for experienced ML practitioners, as well as non-experts to easy access the training platform
Benefits
general: Health Plans
general: Mental Health support
general: a 401(k) Retirement Plan with employer match
general: Stock Option Program
general: Disability Programs
general: Health Savings and Flexible Spending Accounts
general: Family-forming benefits
general: Life and Serious Injury Benefits
general: paid leave of absence programs
general: Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off
general: Full-time salaried employees are immediately entitled to flexible time off
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