I Quit My $130,000 ML Engineer Job After Learning 4 Lessons

2026-03-03 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Entrepreneurship & Start-ups · Depth: Novice, medium

Summary

A machine learning engineer recently departed a Big Tech company despite enjoying a "dream job" with flexible working, smart colleagues, excellent perks, good work-life balance, minimal meetings, and compensation exceeding $100k. The engineer's decision stemmed from a growing lack of motivation and purpose, feeling that the company's large size and established success fostered bureaucracy and a reluctance to take risks or iterate quickly. This culture clashed with the engineer's pragmatic, action-oriented approach. Additionally, extensive internal tooling, while boosting productivity, limited opportunities to develop transferable skills in areas like cloud systems and model deployment. The engineer also found that mature products and algorithms offered minimal scope for substantial impact, leading to work focused more on maintenance than innovation. Consequently, the engineer chose to leave for a startup, seeking greater impact and entrepreneurial challenge.

Key takeaway

For machine learning engineers weighing career paths in large corporations versus startups, consider your personal drive for impact and innovation. If you thrive on rapid iteration, building from scratch, and seeing direct results, Big Tech's mature systems and extensive internal tooling might limit your sense of purpose and skill development. Evaluate roles based on the scope for new feature implementation and the potential for making a tangible "dent" rather than solely on compensation or perks.

Key insights

Big Tech's stability and mature systems can stifle innovation and personal growth for action-oriented engineers.

Principles

• Bureaucracy increases with company size.
• Established success reduces risk-taking.
• Internal tools can hinder skill transferability.

In practice

• Evaluate company culture for alignment.
• Assess skill development opportunities.
• Seek roles with clear impact potential.

Topics

• Machine Learning Engineering
• Big Tech Culture
• Internal Tooling
• Startup Environment
• Career Transition

Best for: Machine Learning Engineer, AI Engineer, Entrepreneur

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