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Software Engineer L4/L5 Training Platform, Machine Learning Platform

Netflix

EngineeringMid Level🇺🇸 United StatesFull Time

Key Details

Work Style
Flexible Hours
Schedule
Standard (40 hrs)
Posted
JAN 22, 2026

Location

Eligibility
🇺🇸 United States
HQ Location
US

Company

Size
1000+ employees
Industry
Entertainment

About This Role

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 Opportunity

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.

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 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

Minimum Job Qualifications

  • 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.

Preferred Qualifications

  • 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

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.

Ready to Escape the Office?

This role is calling your name. Apply directly on the company's website.

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