The Evolving Role of the ML Engineer | Towards Data Science

Towards Data Science

by TDS Editors

February 13, 2026

AI-Generated Deep Dive Summary

Stephanie Kirmer, a seasoned Machine Learning Engineer with nearly a decade of experience in data science and AI, shares insights on how her career has evolved alongside advancements in machine learning, particularly the rise of large language models (LLMs). With a unique background in sociology and education, Kirmer emphasizes the importance of viewing technology through a social lens. She highlights how her sociological training has shaped her perspective on the ethical and societal implications of AI, urging professionals to consider inequalities and diverse impacts when developing and deploying AI solutions. Kirmer discusses the transformative effects of LLMs on her daily work. While these tools have streamlined routine tasks like coding and testing, she acknowledges their limitations and stresses that human expertise remains crucial for tackling complex, unique problems. She cautions against overestimating the value of LLMs, noting that while they offer utility, their potential to generate $200 billion in profit is overstated. This critique suggests a possible "AI economy" bubble driven by inflated expectations rather than realistic returns. Kirmer also addresses the growing skepticism surrounding AI technologies, particularly after a year marked by significant backlash against generative AI. She argues that AI companies have fueled distrust by making unrealistic promises to meet short-term financial goals. To rebuild trust, she advocates for more modest and achievable applications of AI, emphasizing transparency and ethical considerations over sensational claims. Her perspective underscores the need for a balanced approach to AI development—one that prioritizes meaningful impact over hype-driven expectations. By blending her technical expertise with sociological insights, Kirmer offers a nuanced view of the challenges and opportunities facing AI professionals today. Her commentary is particularly relevant for anyone interested in understanding the ethical dimensions of AI, the potential risks of market overhyping, and the importance of fostering trust between AI developers and the public.

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Originally published on Towards Data Science on 2/13/2026