Lead ML Inference Engineer, Advertising
RokuAustin, TexasPosted 24 February 2026
Tech Stack
Job Description
<div class="content-intro"><h2 style="font-family: GothamBold,Helvetica,Arial,sans-serif; color: #662d91;">Teamwork makes the stream work.</h2>
<p> </p>
<h3 style="font-family: GothamBold,Helvetica,Arial,sans-serif;"><strong>Roku is changing how the world watches TV</strong></h3>
<p>Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers.</p>
<p>From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.</p>
<p> </p></div><h3><span data-contrast="auto">About the team </span><span data-ccp-props="{}"> </span></h3>
<p><span data-contrast="auto">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. </span></p>
<p> </p>
<h3><span data-contrast="auto">About the role </span><span data-ccp-props="{}"> </span></h3>
<p><span data-contrast="auto">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 models. </span><span data-contrast="auto">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.</span></p>
<p> </p>
<h3><span data-contrast="auto">What you’ll be doing</span></h3>
<ul>
<li><span data-contrast="auto">Lead the design and development of a SOTA Inference platform </span><span data-ccp-props="{}"> </span></li>
<li><span data-contrast="auto">Oversee the development of monitoring, observability, and other tooling to ensure system and model performance, reliability, and scalability of online inference services</span></li>
<li><span data-contrast="auto">Identify and resolve system inefficiencies, performance bottlenecks, and reliability issues, ensuring optimized end-to-end performance</span><span data-ccp-props="{}"> </span></li>
<li><span data-contrast="auto">Stay at the forefront of advancements in inference frameworks, ML hardware acceleration, and distributed systems, and incorporate innovations where and when they are impactful</span></li>
</ul>
<p> </p>
<h3><span data-contrast="auto">We’re excited if you have</span></h3>
<ul>
<li><span data-contrast="auto">M.S. or above in CS, ECE, or a related field</span><span data-ccp-props="{}"> </span></li>
<li><span data-contrast="auto">10+ years of experience in developing and deploying large-scale, distributed systems, with at least 5 years in a leadership or technical lead role </span><span data-ccp-props="{}"> </span></li>
<li><span data-contrast="auto">Strong programming skills in high-performance languages</span></li>
<li><span data-contrast="auto">Deep understanding of inference frameworks and ML system deployment</span></li>
<li><span data-contrast="auto">Proven experience optimizing pe ... (truncated, view full listing at source)
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