Software Engineer (L4), Consumer ML Model Compute & Serving Systems

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Written by AI from the job title and company, not by Netflix, so some details may not match this job. The employer's own page has the real description.

Overview

As a Software Engineer (L4) in the Consumer ML Model Compute & Serving Systems team at Netflix, you will play a pivotal role in designing, implementing, and optimizing machine learning models that are integral to enhancing user experience across our streaming platform. You will collaborate with cross-functional teams to ensure the effective deployment and scalability of ML solutions, while maintaining high standards for performance and reliability. Your expertise will not only contribute to the development of cutting-edge technology but also directly impact how millions of users interact with our services.

Job Responsibilities

• Design and implement scalable ML model serving infrastructure to ensure efficient data processing and user interactions.
• Collaborate with data scientists and product teams to translate business needs into technical specifications for ML models.
• Optimize the performance of existing ML pipelines to improve response times and resource utilization.
• Monitor and troubleshoot production issues to maintain system reliability and responsiveness.
• Contribute to the CI/CD pipeline for ML model deployment ensuring seamless updates and rollback capabilities.
• Document architecture and design options, workflows, and model performance to establish best practices.

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• A minimum of 5 years of software engineering experience, preferably in building and servicing ML models.
• Proficient in languages such as Python, Java, or C++ with a strong background in software development principles.
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and model serving platforms.
• Solid understanding of cloud computing services (e.g., AWS, Google Cloud) and infrastructure as code.
• Strong analytical and problem-solving abilities with a focus on scalability and performance optimization.

Technologies & Tools

This position involves using a variety of technologies and tools, including advanced machine learning frameworks such as TensorFlow and PyTorch, cloud services like AWS and Google Cloud for deployment scalability, as well as containerization and orchestration tools like Docker and Kubernetes. Familiarity with monitoring and analytics tools will be advantageous for performance tuning and troubleshooting.

Ideal Candidates

The ideal candidate for this role possesses strong technical acumen combined with an ability to think strategically about the impact of their work on end users. They are collaborative, open to continuous learning, and thrive in a dynamic, fast-paced environment. Passion for machine learning and data-driven decision-making, along with a proactive problem-solving attitude, are essential qualities that will help drive success in this position.