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    ML Engineer, Generative AI Core Infra - Los Gatos, CA, United States - Netflix

    Netflix
    Netflix Los Gatos, CA, United States

    Found in: Jooble US O C2 - 7 hours ago

    Netflix background
    Description

    Netflix is the world's leading streaming entertainment service with 260+ million paid memberships in over 190 countries enjoying TV series, documentaries, and feature films across a wide variety of genres and languages. Machine Learning drives innovation across all product functions and decision-support needs. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation.

    The Opportunity We are looking for driven and experienced Machine Learning Engineers to join a new team, GenAI Core Infra, under our Machine Learning Platform (MLP) org. MLP's charter is to maximize the business impact of all ML at Netflix. We develop innovative ML infrastructure to support key product functions such as personalized recommendations, studio algorithms, virtual productions, growth intelligence, and content demand modeling among others. In this role, you will scale the training, customization, fine-tuning, and serving capabilities for large language and multi-modal foundation models. You will partner closely with ML researchers and data scientists to address critical performance, usability, and scalability challenges that come with training and tuning generative foundation models at the Netflix scale. Responsibilities
    • Ensure that training, fine-tuning, and inference jobs can meet performance, throughput, and cost efficiency needs for various multi-modal use cases.
    • Evaluate and possibly integrate academic, OSS or enterprise training and inference performance optimizations.
    • Design and implement tooling around the productization of generative foundation models such as RAG, version control, prompt management.
    • Design easy-to-use APIs and interfaces for experienced ML practitioners, as well as non-experts.
    Minimum Job Qualifications
    • 2-5 years of experience in ML engineering on production systems dealing with training or inference of deep learning models.
    • MS/PhD in Computer Science, or a related field
    • Experience with at least one of PyTorch, Tensorflow, or JAX performance tuning and optimization
    • Development, debugging, and optimization experience on GPUs and other accelerators and associated software ecosystem
    • Development, debugging, and optimization experience on NVIDIA GPUs
    • Experience with cloud computing providers such as AWS
    • Comfortable with ambiguity, ability to take on and execute 0-1 projects
    • Experience partnering closely with ML researchers
    • Excellent written and verbal communication skills.
    Preferred Qualifications
    • Experience with training, fine-tuning, or serving large deep learning models, or LLMs, at the scale of millions of users.
    • Development, debugging, and optimization experience on GPUs and other accelerators and associated software ecosystem
    • Experience adapting OSS CUDA kernels, or writing your own to maximize training or inference performance of deep learning models.
    • Experience with popular optimized LLM serving libraries such as DeepSpeed, TensorRT, or vLLM.
    • Experience with large-scale distributed training and different parallelism techniques for scaling up training, such as FSDP and tensor/pipeline parallelism.
    • Experience with cloud computing providers such as AWS
    Recent Artifacts from the team
    • Invited Paper at RecSys InTune: RL based pipeline optimization for Deep RecSys
    • Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
    • Talk on heterogeneous compute environments for ML at Ray Summit 2023
    • OSS LLM Serving & Benchmarking - Talk at ML Platform Meetup Dec 2023
    • Opportunities for OSS Gen AI in the Enterprise - Panel Discussion at ML Platform Meetup Dec 2023
    What do we offer? 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 - $720,000. 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. Netflix is a unique culture and environment. Learn more here. #J-18808-Ljbffr
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