Lead ML Inference Engineer; Advertising

Requirements

• We’re looking for a strong technical leader with deep experience in ML serving, high-performance computing, and industry standard frameworks - someone excited to mentor engineers, innovate at scale, and shape the future of machine learning at Roku
• M.S. or above in CS, ECE, or a related field
• 10+ years of experience in developing and deploying large-scale, distributed systems, with at least 5 years in a leadership or technical lead role
• Strong programming skills in high-performance languages
• Deep understanding of inference frameworks and ML system deployment
• Proven experience optimizing performance for large-scale machine learning systems, including a deep knowledge of SOTA model optimizations, hardware-software co-design, GPU acceleration, and HPC techniques
• Excellent communication and collaboration skills
• Experience leading teams working on high-throughput, low-latency ML serving systems
• Experience collaborating with and leading global, cross-functional teams
• Contributions to open-source ML or systems projects

• The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and Roku
• The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across distributed systems at large scale and with low latency. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems
• At the core of this is our Machine Learning and Inference Platform that powers the entire landscape
• In this role, you will architect, design, and lead the development of a SOTA Inference platform that can handle Advertising-level low latencies, scale, throughput, and availability with optimizations that span across hardware, software, and model
• Lead the design and development of a SOTA Inference platform
• Oversee the development of monitoring, observability, and other tooling to ensure system and model performance, reliability, and scalability of online inference services
• Identify and resolve system inefficiencies, performance bottlenecks, and reliability issues, ensuring optimized end-to-end performance
• Stay at the forefront of advancements in inference frameworks, ML hardware acceleration, and distributed systems, and incorporate innovations where and when they are impactful