<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Industry | Yisheng Zhong</title><link>https://easonzhong99.github.io/tags/industry/</link><atom:link href="https://easonzhong99.github.io/tags/industry/index.xml" rel="self" type="application/rss+xml"/><description>Industry</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 15 Jun 2026 00:00:00 +0000</lastBuildDate><image><url>https://easonzhong99.github.io/media/icon_hu68170e94a17a2a43d6dcb45cf0e8e589_3079_512x512_fill_lanczos_center_3.png</url><title>Industry</title><link>https://easonzhong99.github.io/tags/industry/</link></image><item><title>Summer 2026 AI/ML Intern at The Washington Post</title><link>https://easonzhong99.github.io/post/washington-post-internship-2026/</link><pubDate>Mon, 15 Jun 2026 00:00:00 +0000</pubDate><guid>https://easonzhong99.github.io/post/washington-post-internship-2026/</guid><description>&lt;p>This summer I joined &lt;strong>The Washington Post&lt;/strong> in Washington, D.C. as an &lt;strong>Artificial Intelligence / Machine Learning intern&lt;/strong>, as part of the &lt;em>2026 Engineering &amp;amp; Business Operations Internship Program&lt;/em>.&lt;/p>
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&lt;figure id="figure-day-one-of-the-washington-post-2026-engineering--business-operations-internship-program">
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&lt;div class="w-100" >&lt;img alt="My desk on day one of the internship program" srcset="
/post/washington-post-internship-2026/welcome-desk_hud51eff7d3c64eea9b4166d840cedcbab_295909_965be3d0364e1b9e83cf2b5b19a5ea52.webp 400w,
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src="https://easonzhong99.github.io/post/washington-post-internship-2026/welcome-desk_hud51eff7d3c64eea9b4166d840cedcbab_295909_965be3d0364e1b9e83cf2b5b19a5ea52.webp"
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&lt;/div>&lt;figcaption>
Day one of The Washington Post 2026 Engineering &amp;amp; Business Operations Internship Program
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&lt;p>Working inside a newsroom gave me a very different view of the problems I study. In research I get to choose the benchmark; here the constraints came from real editorial workflows, real latency budgets, and a real bar for accuracy — where a plausible-but-wrong model output is not a metric regression but a credibility cost.&lt;/p>
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&lt;figure id="figure-inside-the-newsroom">
&lt;div class="flex justify-center ">
&lt;div class="w-100" >&lt;img alt="The Washington Post newsroom" srcset="
/post/washington-post-internship-2026/newsroom_hucc44807a82bc04a333733a241d626068_284723_a908ffff0e0701ab7a95cfba43a8f8b4.webp 400w,
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src="https://easonzhong99.github.io/post/washington-post-internship-2026/newsroom_hucc44807a82bc04a333733a241d626068_284723_a908ffff0e0701ab7a95cfba43a8f8b4.webp"
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Inside the newsroom
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&lt;p>I&amp;rsquo;m grateful to my team and mentors for their guidance, and to the other interns for making the summer as much fun as it was instructive. It was a good reminder of why the trustworthiness questions in my research — what a model remembers, what it should be able to forget, and how confidently it should speak — matter well beyond the paper.&lt;/p></description></item></channel></rss>