Software Engineer Training Platform @ Netflix
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Overview
Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries. As a Software Engineer Training Platform at Netflix, you will build and maintain a highly scalable machine learning model training platform used across the company.
The Opportunity
You will join the Training Platform team under the Machine Learning Platform (MLP) organization. Your role is to design and build systems for large-scale model training, fine-tuning, model transformation, and evaluation workflows that support various high-impact ML use cases at Netflix.
Key Responsibilities
- Design and build scalable ML training infrastructure
- Optimize systems to improve cost-effectiveness and scale
- Create easy-to-use APIs for ML practitioners and non-experts
- Collaborate across teams and geographies
- Implement observability, logging, and on-call practices
Minimum Qualifications
- Experience in ML engineering on production systems
- Track record of operating large-scale ML infrastructure
- Comfortable with cloud computing providers, preferably AWS
- Excellent communication skills and team collaboration
Preferred Qualifications
- Knowledge of modern ML development workflows
- Experience with cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks)
- Expertise in distributed training techniques and generative AI
Benefits and Culture
Netflix offers competitive annual salaries along with flexible compensation options. Comprehensive benefits include health plans, 401(k) with employer match, stock options, paid time off and more. Diversity and inclusion are core values and the company provides a supportive, collaborative work environment.
Key skills/competency
- Machine Learning
- Infrastructure
- Training
- API Design
- Cloud Computing
- Distributed Systems
- Cost Optimization
- Observability
- Collaboration
- Scalability
How to Get Hired at Netflix
🎯 Tips for Getting Hired
- Customize your resume: Highlight scalable ML system experience.
- Show cloud expertise: Emphasize AWS and cloud projects.
- Demonstrate collaboration: Detail cross-team project involvement.
- Prepare technical insights: Practice distributed systems and API design questions.