Skillenai
We analyzed 3,277 job postings for Data Scientists, ML Engineers, and AI Engineers across four seniority levels. The biggest compensation jump isn’t between mid and senior — it’s Senior to Staff, where ML Engineers earn a median $59K more. And most ‘hot’ AI skills (Generative AI, prompt engineering) disappear as premium drivers once you control for level. Here’s the data.
We analyzed 77,735 job postings and found that the Bay Area tech market is statistically distinct from everywhere else. Rest-of-US and non-US skill profiles are indistinguishable for Data Scientists and AI Engineers. Here’s what the chi-square tests reveal.
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What 3,215 Job Postings Reveal About Skills That Define each role Ask five people the difference between a Data Scientist, ML Engineer, and AI Engineer, and you’ll get five different answers. I decided to let the data speak for itself. I built a data pipeline that ingests 10,000 tech job postings and 50,000 learning resources […]
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The Next Architectural Layer Beyond Agentic AI OpenClaw and its variants have taken the world by storm. In this article I argue that they represent a new architectural layer beyond what has come to be known as “AI agents” or “agentic AI”. Today’s agents are merely ephemeral goal strivers that seek to achieve a user’s […]
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A Solution to the Challenges AI Teams Are Facing Across industries, many AI teams are feeling a similar strain: high expectations, compressed timelines, and uncertainty about how to execute well. I have seen this pattern firsthand, heard it from other AI leaders, and watched it emerge repeatedly in public discussions. In this article, I outline […]
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AI Engineering was recently named the #1 fastest growing job by LinkedIn, with data science cited as one of the top roles transitioned from. Meanwhile, every data scientist is in the middle of at best an identity crisis and at worst an existential crisis. I recently switched from data science to AI engineering. That transition […]
Very Insightful, so thank you. Have followed a similar path and have a similar sentiment. Also taking the AIM bootcamp at present.
Glad to hear this resonated. I still feel that the AIM AI Engineering Bootcamp was helpful. Perhaps they need two tracks though, one for AI Science and one for AI Engineering. I think it’s hard to optimize for both at the same time.
What a great read about the current situation in our sector! Thank you for sharing. I’m in Italy, and considering we were already behind schedule on ML and Data, the rush to implement “”AI”” is completely ridiculous…
Thanks! I just published another piece on how the pressure to ship AI features is creating chaos within AI teams: https://skillenai.com/2026/01/25/the-ai-lifecycle/
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In the 90s everyone talked about the “information superhighway” that was created by the internet and fiber optics. AI has the same potential to become an “intelligence superhighway.” But that potential is unrealized today and instead feels like driving at rush hour. AI is Like an Automobile Let’s stick with transportation analogies to help explore […]
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The world is transitioning from the Information Age to the Intelligence Age. We need a new set of mental models to thrive in this new age. Below are a few of the mental models I’ve adopted recently to help guide my work as an AI engineer. Engine in a Buggy Don’t put an engine in […]
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A Human Story: Sarah, a 32-year-old teacher, spent two years chasing elusive answers about her health. When she first felt tingling in her arm and unexplained fatigue, each doctor visit ended with normal blood tests and advice to “wait it out.” A conventional MRI eventually showed a few tiny brain lesions, but no one was […]
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Introduction: The Convergence of Senses in Machines Imagine a toddler hearing a dog bark for the first time. She turns her head, sees the furry creature, and instinctively connects the sound to its source. This seamless association between sight and sound is fundamental to how humans understand the world. Now, picture a machine doing the […]
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