Posts in insights-and-analytics.
We swept 156,928 job postings and 435,000+ blog and news articles for ~90 LLM-eval frameworks and ~55 evaluation methodologies. The result: hiring names a tiny tool set (LangSmith + Langfuse = 56% of all eval-tool mentions; no framework over 1%), practitioners are converging on LLM-as-a-judge with a rubric, and the benchmarks the press argues about — SWE-bench, MMLU, GPQA — show up in roughly zero job descriptions.
A popular framing says Product Engineer sits at the intersection of Product Manager and Software Engineer. We measured the overlap across 35,067 job postings. There is no fat intersection region. Product Engineer is a modern-stack SWE archetype.
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.
Python appears in 29% of tech postings (2x SQL) and uniquely dominates both edges of our knowledge graph — most-required by jobs AND third-most-mentioned in docs. Plus the salary twist: Python is the floor, not the premium.
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.
A press rumor said OpenAI is building a phone. We pulled all 746 OpenAI postings; 19 sit on a team called "Consumer Devices." But the team is 90% software and AI research, with no in-house industrial design or mechanical engineering. The hardware roles are procurement and integration. The shape of the team is Google-in-2007 (build an OS, ship it on a partner's hardware), not Apple-in-2007 (build it all yourself).
SpaceX now holds a call option to buy Cursor for $60B. We stress-test the thesis that both xAI and Cursor would die in 12 months without this deal, using 125K enriched job postings and a knowledge-graph view of mentions, co-mentions, and internal hiring stacks.
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.
