The Evolving Role of the ML Engineer

2026-02-13 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, AI Ethics & Societal Impact · Depth: Intermediate, short

Summary

Stephanie Kirmer, a Staff Machine Learning Engineer with nearly a decade of experience, shares her perspective on AI's social impacts, the evolution of ML engineering with Large Language Models (LLMs), and the current state of the AI economy. Her background in sociology profoundly influences her approach, prompting questions about social inequalities, diverse experiences, and institutional influences in AI. Kirmer notes that LLM-powered code assistants are changing daily ML work by aiding in idea generation, critiques, and boilerplate tasks like unit test writing. She expresses concern that the AI economy is a bubble driven by unrealistic investment expectations, not the underlying technology's utility, which she believes is not commensurate with the hundreds of billions invested. Kirmer also suggests that AI companies could rebuild public trust by abandoning outlandish promises and focusing on practical applications, alongside a broad public education campaign to demystify the technology.

Key takeaway

For AI Product Managers evaluating market strategies, recognize that current AI investment levels are likely unsustainable due to inflated expectations. Focus your product development on demonstrable, practical applications rather than speculative "moonshots" to build genuine user value and avoid contributing to a potential market correction. Prioritize transparent communication about AI capabilities to foster public trust.

Key insights

Sociological inquiry provides a critical lens for understanding AI's societal impacts and economic dynamics.

Principles

• AI hype drives public skepticism.
• LLMs are useful but overvalued.
• Sociology informs AI impact analysis.

Method

A sociological method involves forming hypotheses about social phenomena, such as AI's impact, and then seeking evidence to prove or disprove them, focusing on inequalities and institutional influences.

In practice

• Use LLMs for boilerplate code.
• Employ LLMs for idea generation.
• Critique problem approaches with LLMs.

Topics

• Social Impacts of AI
• Large Language Models
• AI Economic Bubble
• LLM Evaluation Strategies

Best for: Machine Learning Engineer, AI Ethicist, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.