ML Engineer L5, LLM Application Frameworks, Machine Learning Platform

Found in: beBee jobs US - 2 weeks ago


Los Gatos, California, United States Netflix Full time

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 OpportunityWe are looking for a driven Software Engineer to join a new team focussed on Foundation Model 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 get to:

Define the LLM and Foundation Model Application frameworks with agentic approaches for large-scale language applications
Design and implement tooling around the productization of generative foundation models such as RAG, version control, and 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.
Experience with LLM Ops tooling, LLM Agent APIs, and LLM orchestration frameworks and libraries like LangChain
BS/MS in Computer Science, or a related field
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.
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 LLMs in the Enterprise - Panel Discussion at ML Platform Meetup Dec 2023

Netflix focuses on Digital Entertainment. Their company has offices in Los Gatos. They have a very large team that's 5000+ employees. To date, Netflix has raised $3.102B of funding; their latest round was closed on April 2018.

You can view their website at or find them on LinkedIn.


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