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The Evolving Role of the ML Engineer

Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, and how her day-to-day work changed with the ...

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The Evolving Role of the ML Engineer
Source: Towards Data Science

What’s Happening

Okay so Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, and how her day-to-day work changed with the rise of LLMs.

The post The Evolving Role of the ML Engineer appeared first on Towards Data Science. In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. (let that sink in)

Today, we’re thrilled to our conversation with Stephanie Kirmer .

The Details

Stephanie is a Staff ML Engineer, with almost 10 years of experience in data science and ML. Before, she was a higher education administrator and taught sociology and health sciences to undergraduate students.

She writes a monthly post on TDS about social themes and AI/ML, and gives talks around the country on ML-related subjects. She’ll be speaking on strategies for customizing LLM evaluation at ODSC East in Boston in April 2026.

Why This Matters

You studied sociology and the social and cultural foundations of education. How has your background shaped your perspective on the social impacts of AI? I think my academic background has shaped my perspective on everything, including AI.

This adds to the ongoing AI race that’s captivating the tech world.

Key Takeaways

  • ”, “how do different kinds of people experience this thing differently?
  • ”, and “how do institutions and groups of people influence how this thing is happening?
  • Those are the kinds of things a sociologist wants to know, and we use the answers to develop an understanding of what’s going on around us.

The Bottom Line

I’m actually in the process of writing a new piece about this. I think the progress of code assistants using LLMs is fr fascinating and is changing how a lot of people work in ML and in software engineering.

What do you think about all this?

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