About the position
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. We 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.
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 easily access the training platform.
Requirements
- Experience 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 cases.
- Experience with cloud computing providers, preferably AWS.
- Comfortable with ambiguity and working across multiple layers of the tech stack to execute on both 0-to-1 and 1-to-100 projects.
- Adopt and promote best practices in operations, including observability, logging, reporting, and on-call processes to ensure engineering excellence.
- Excellent written and verbal communication skills.
- Comfortable working in a team with peers and partners distributed across (US) geographies & time zones.
Nice-to-haves
- Understand modern and real-world Machine Learning model development workflows and experience partnering closely with ML modeling engineers.
- Familiarity 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 parallelism.
- Expertise in the area of Generative AI, specifically when it comes to training foundation models, fine tuning them, and distilling them to smaller models.
Benefits
- Health Plans
- Mental Health support
- 401(k) Retirement Plan with employer match
- Stock Option Program
- Disability Programs
- Health Savings and Flexible Spending Accounts
- Family-forming benefits
- Life and Serious Injury Benefits
- 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.
