Principal ML Infrastructure Engineer, Tubi

• 10+ years of experience in software engineering, with a significant focus on building and scaling large-scale distributed systems
• Bachelor's or Master's degree in Computer Science or a related field
• Proven experience as a technical leader, architecting and designing complex systems, preferably in the ML infrastructure domain
• 5+ years of experience with databases, caching technologies, and message brokers
• Expertise in Scala, Java, Python programming languages
• Extensive experience with cloud platforms (preferably AWS)

Desirable

• As a Principal Engineer on the ML Infrastructure team, you will be a technical leader and visionary, driving the evolution of our machine learning platform
• You will tackle the most complex and impactful technical challenges, shaping the architecture and technology choices that enable our ML capabilities to scale and deliver exceptional user experiences
• You will be a key influencer, bridging the gap between engineering and product, and a mentor to senior engineers, fostering a culture of technical excellence and continuous improvement
• Your work will be used by millions of users
• Vision & Influence: Define and champion the long-term vision for ML infrastructure, aligning it with company goals and industry best practices. Influence technical direction and technology selection across the ML platform
• Strategic Roadmap: Develop and maintain a roadmap (6-12 months) for the ML Infra team, anticipating future needs and proactively addressing emerging trends
• Innovation & Optimization: Identify opportunities to improve ML infrastructure efficiency, scalability, and performance. Research and advocate for new technologies and approaches to optimize the ML development lifecycle
• System Design: Lead the architecture and design of complex ML systems, ensuring scalability, reliability, security, and maintainability
• Distributed Systems Expertise: Design and build scalable, high-throughput, and/or low-latency distributed systems using Scala and related technologies. This includes expertise in areas like distributed databases, message queues, and stream processing
• Quality & Standards: Champion and enforce engineering best practices, including code quality, testing, and documentation. Contribute to the development and implementation of ML infrastructure standards
• Technical Problem Solving: Resolve critical and complex technical challenges related to ML infrastructure, demonstrating expertise in debugging, performance optimization, and system troubleshooting
• Project Execution: Lead and deliver complex ML infrastructure projects, effectively managing scope, timelines, and dependencies. Mentor engineers on project management best practices
• Collaboration & Mentorship: Foster a collaborative environment and provide technical mentorship to other engineers, enabling their growth and development
• Cross-functional Partnership: Collaborate effectively with data scientists, ML engineers, and product managers to understand their needs and translate them into infrastructure solutions
• Stakeholder Management: Communicate effectively with stakeholders at all levels, including senior leadership. Clearly articulate technical concepts, progress updates, and roadblocks
• Knowledge Sharing: Promote knowledge sharing and best practices across the organization through documentation, presentations, and mentorship