Netflix is looking to accelerate the development, scaling, and streamlining of machine learning projects by empowering scientists to focus more on their core science impact and less on operational complexities.
Requirements
- Experience working with large, unstructured data such as video, audio, or images.
- Demonstrated expertise in machine learning operations (MLOps), including model deployment, monitoring, and maintenance.
- Deep understanding of machine learning or data science concepts, including feature engineering, data pipelines, and end-to-end model lifecycle management.
- A Python and SQL pro with experience in ML frameworks like PyTorch, crafting code that's clear, concise, and clean.
- Solid foundation in software engineering principles and best practices.
Responsibilities
- Collaborate closely with data scientists and engineers to operationalize and maintain machine learning models in production, ensuring high availability, scalability, and performance.
- Develop, implement, and advocate for best practices in model deployment, monitoring, and lifecycle management.
- Work with a wide range of structured and unstructured data, including video, audio, and images.
- Establish robust processes for data backfilling, version control, and automated testing.
- Abstract common workflows into reusable tools and frameworks to accelerate ML-driven insights.
- Partner with Platform teams to design and maintain reliable systems for tracking, auditing, and managing model dependencies and changes.
- Champion excellence in both software engineering and machine learning practices.
Other
- Self-motivated, proactive, and comfortable working in a fast-paced, dynamic, and sometimes ambiguous environment.
- Excellent communicator, able to tailor your message to different audiences while maintaining clarity and impact.
- Enthusiastic about and compatible with Netflix culture.
- Collaborate with cross-functional teams to uphold high standards for model robustness, transparency, and regulatory compliance.
- Work seamlessly across diverse business domains and with colleagues from varied backgrounds.
