#machine learning engineer

We scored 222 AI/ML/DS skills on five independent difficulty signals. Out of 222, exactly one scores positive on all five: JAX — Google's numerical-computing library that powers Gemini, Gemma, and parts of Claude.

We analyzed 9,000+ job postings across DS, MLE, AIE, Applied Scientist, and Research Scientist. The popular DS-to-AI Engineer narrative has the geometry backwards: it's the longest jump on the board. The shortest jumps run through the training-flavored half of ML Engineer.

AI Engineer postings mention fine-tuning more than any other role (26.2% vs 18.2% MLE vs 4.9% DS) — but a deep dive into 6,802 jobs shows the technical fine-tuning toolkit (LoRA, RLHF, distillation, post-training) actually lives in ML Engineer postings.

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.

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…