Director, ML Engineering & Infrastructure

优缺点分析

角色解读

• This role offers a path to senior executive positions (e.g., VP of Engineering, CTO) by providing experience in leading large-scale technical strategy and managing complex, high-impact teams at the intersection of ML and infrastructure.
• It allows for deep specialization in cutting-edge ML applications for media and entertainment, positioning you as an industry expert in streaming technology and personalization systems.
• Lead the strategic development and scaling of machine learning systems, including recommendation engines and personalization algorithms, to enhance user experience on a streaming platform.
• Architect and oversee the deployment of robust, distributed infrastructure capable of handling massive-scale ML workloads, ensuring system reliability and performance.
• Manage and mentor high-performing teams of ML engineers and infrastructure specialists, fostering a collaborative and innovative work environment.
• Deep expertise in end-to-end ML system development, including model training, deployment pipelines, and large-scale serving, with hands-on experience in frameworks like TensorFlow or PyTorch.
• Strong background in designing and managing distributed systems and cloud infrastructure (specifically AWS), with a focus on scalability, low latency, and fault tolerance.
• Proven leadership and team management skills, with the ability to set technical strategy, align cross-functional teams, and drive project execution in a fast-paced environment.

申请策略

• Research Tubi's content library and user experience to understand their business context
• be prepared to discuss how your ML expertise can directly impact their goals in recommendations and ads.
• Given the hybrid team focus, emphasize examples of successfully bridging ML engineering and infrastructure work, and your collaborative approach with product and content teams.
• Quantify your experience leading ML projects, especially those involving recommendation/personalization systems and scaling to millions of users. Highlight specific metrics (e.g., improved engagement, reduced latency).
• Detail your hands-on experience with key technologies mentioned: AWS, Databricks, TensorFlow/PyTorch, and any specific distributed systems or streaming platforms you've architected.
• Showcase your leadership achievements: describe teams you've built or managed, strategic roadmaps you've defined, and how you've influenced product or technical direction.
• If needed, deepen practical knowledge of Databricks for large-scale data workflows and ensure familiarity with the latest AWS services for ML and big data (e.g., SageMaker, EMR, Kinesis).
• Brush up on system design principles for high-availability, low-latency serving architectures and modern MLOps practices for monitoring and evaluating production ML systems.

面试指南

• Use the STAR method (Situation, Task, Action, Result) to structure answers, focusing on your specific role, the actions you took, and quantifiable outcomes or learnings.
• For technical/architectural questions, start by clarifying requirements, then outline your design rationale, considering trade-offs (e.g., latency vs. cost, simplicity vs. scalability), and conclude with how you'd validate and monitor the solution.
• 'Walk us through your experience designing and deploying a large-scale recommendation or personalization system. What were the biggest technical challenges and how did you overcome them?'
• 'Describe a time you had to architect a distributed system to handle a significant increase in ML workload. How did you ensure scalability, reliability, and cost-efficiency?'
• 'How do you approach building and mentoring a high-performing technical team? Share an example of a team you developed and the outcomes achieved.'
• 'How would you design the ML infrastructure and pipeline for a new feature at Tubi, considering data ingestion, model training, A/B testing, and serving?'
• 'Tell us about a time you had to influence a product or business strategy based on technical constraints or ML insights.'
• Prepare 2-3 detailed case studies from your past work that demonstrate end-to-end ownership of ML systems, leadership in technical strategy, and successful team management.