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In this episode of Ship Talk, host Dewan Ahmed (Principal Developer Advocate, Harness) sits down with Marina Petzel, Senior ML Engineer and AI Productivity Lead at Autodesk, to unpack what it actually looks like to ship AI into long-lived, production software.
Marina shares her journey from classic predictive analytics to computer vision and LLMs, and how her team brings intelligent features like Smart Blocks in AutoCAD from ideation to prototype to production on a tight yearly release cadence. She breaks down the often ignored parts of ML work: data pipelines, infrastructure, UX fit, and safety testing, and explains why “garbage in, garbage out” is a law of nature, not just a cliché.
They also dive into AI for developer productivity, multi-agent workflows, red-teaming and prompt safety, and how every engineer, regardless of seniority, can start using AI as a genuine skill multiplier. Marina closes with concrete advice for aspiring ML engineers: build more, read less, follow your curiosity, and share your work publicly so your visibility compounds over time.
Follow Marina's work: https://marina.petzel.tech
Connect with her: https://www.linkedin.com/in/marina-petzel
By By Harness5
11 ratings
In this episode of Ship Talk, host Dewan Ahmed (Principal Developer Advocate, Harness) sits down with Marina Petzel, Senior ML Engineer and AI Productivity Lead at Autodesk, to unpack what it actually looks like to ship AI into long-lived, production software.
Marina shares her journey from classic predictive analytics to computer vision and LLMs, and how her team brings intelligent features like Smart Blocks in AutoCAD from ideation to prototype to production on a tight yearly release cadence. She breaks down the often ignored parts of ML work: data pipelines, infrastructure, UX fit, and safety testing, and explains why “garbage in, garbage out” is a law of nature, not just a cliché.
They also dive into AI for developer productivity, multi-agent workflows, red-teaming and prompt safety, and how every engineer, regardless of seniority, can start using AI as a genuine skill multiplier. Marina closes with concrete advice for aspiring ML engineers: build more, read less, follow your curiosity, and share your work publicly so your visibility compounds over time.
Follow Marina's work: https://marina.petzel.tech
Connect with her: https://www.linkedin.com/in/marina-petzel

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