Grounded Intelligences

Category: Career & Strategy

Unconventional career paths, job search strategy, and positioning for AI-era roles.

  • The AI Roles Worth Targeting in 2026: A Field Guide for Unconventional Candidates

    I spent 19 years in defense finance and digital transformation before becoming someone companies want for AI work, and here is what I see in 2026: the most interesting AI roles are not being filled by candidates submitting the standard machine learning pipeline resume. They are being filled by people who understand both the technology and the domain it’s being applied in. The pure-ML resume is now common enough to be a commodity. The combination is rare enough to be a differentiator.

    Where Domain Expertise Beats Pure ML Background

    Several roles reward domain depth over model depth. The AI Solutions Architect needs system design thinking and domain credibility — the job is fitting AI into a real organization with real constraints, and knowledge of model internals alone doesn’t get you there. The AI Product Manager needs to understand user behavior and organizational dynamics; candidates from a pure ML background often struggle here precisely because the hard part isn’t the model. The AI Ethicist or Responsible AI Lead draws on philosophy, law, social science, and clinical backgrounds — none of which appear on a standard ML résumé. And the ML Evaluator or RLHF Specialist needs cognitive science, linguistics, and domain expertise far more than systems engineering. In each case the rare skill is the human and organizational understanding, not the technical baseline.

    A Case Study in Recombination

    My own path is the argument. A Licensed Marriage and Family Therapist license, a master’s in Industrial/Organizational Psychology, 19 years in defense finance at Lockheed Martin, and a track record building AI systems that produced quantified outcomes — $360K in annual cost avoidance on a major defense aircraft program Withholds automation, 415,000 tax scenarios processed in 4 hours through the internal tax scenario classifier. None of those pieces gets you the job on its own. A therapist isn’t an AI engineer; an I/O psychologist isn’t a finance lead; a defense finance background isn’t responsible-AI expertise. Together, though, they represent something genuinely uncommon: a person who can build the system and understand the human, organizational, and regulatory context it has to live inside.

    What Companies Want but Can’t Articulate

    What organizations are actually hiring for — even when they can’t put it in a job description — is someone who will catch the problems that emerge at the intersection of the model and the organization. The model that is technically correct but organizationally wrong. The system that is accurate but not trusted by the people meant to use it. The tool that performs beautifully in evaluation and fails in deployment because nobody accounted for how humans actually behave around it. Those failures don’t show up in a model card. They show up six months after launch, and the person who can foresee them is worth more than the person who can squeeze another point of accuracy out of a training run.

    How to Position as an Unconventional Candidate

    The move that works is building in public. Every project you document is evidence — not a claim about what you could do, but a record of what you did. This site is the argument: posts about building real systems, quantified outcomes, and the behavioral science lens most AI engineers don’t carry. A working job search dashboard at jobs.groundedintelligences.com tells a story no resume bullet can, because it’s a live system someone can actually use and inspect. When the standard candidate hands over a list of skills, the unconventional candidate hands over a working thing and says: I built that, here’s what it does, here’s what it cost, here’s what it produced.

    The Most Important Credential in 2026

    The credential that matters most now is a portfolio of working AI systems with documented outcomes. Not a certification, not a bootcamp completion, not a GitHub repository full of Jupyter notebooks that ran once on a laptop — working systems with real usage and quantified results. The bar has risen because building has gotten easier, which means the proof of competence is no longer knowing how but having done. A deployed system with users and numbers settles the question that a credential only gestures at. I wrote about the discipline behind shipping those systems in lessons from 17 enterprise projects.

    What Transfers

    These positioning principles apply in any field where the standard path is well-worn and the non-standard path creates genuine differentiation. When everyone arrives with the same resume, the credential stops distinguishing anyone — and the person who combines an unusual background with demonstrable work becomes legible in a way the conventional candidate can’t. Build the thing, document the outcome, and let the combination of skills nobody else has stacked become the reason you’re the obvious hire. The unconventional path is only a liability until you make it the point.

    → Building something at the intersection of AI, edge computing, and behavioral science? Let’s connect.

  • Building My Career Intelligence Dashboard: A Personal AI Job Search Engine

    I built a fully functional career intelligence platform running at jobs.groundedintelligences.com at zero hosting cost, doing AI-powered job matching and processing hundreds of job postings every day. It is not a demo or a prototype — it ingests live job data, scores it against my profile, and surfaces the best matches every morning, all on Cloudflare’s free tiers. Here is how it’s put together and the one design decision that mattered most.

    The Stack

    The whole thing sits on Cloudflare primitives. A Worker acts as the API server. D1 holds the job database. KV handles rate limiting and session caching. R2 stores resume versions and company logos. Workers AI generates the embeddings that drive job-to-resume matching. That’s five services, each on its free tier, composing into a real application — no virtual machines, no container bills, no separate database host. The same architecture approach underlies my personal finance PWA on Cloudflare Workers, which runs on the same foundation.

    Data Sources

    The data comes from three kinds of sources. Job board APIs — Greenhouse, Lever, and Ashby all expose free public endpoints — provide the bulk of structured listings. Curated RSS feeds from the careers pages of target companies fill in the roles those APIs miss. And a custom scraper Worker handles sites that have no API at all, pulling listings on a schedule and depositing them into D1 alongside the structured data. Together these feed a steady stream of new postings without any paid data subscription.

    The ICP Scoring System

    At the center is an Ideal Candidate Profile stored in D1 that weights the factors I care about — industry, company stage, required-background match, title level, and location preference. Every ingested job gets an ICP score from 0 to 100, calculated as the cosine similarity between the job’s embedding and the profile’s embedding, both produced by Workers AI with bge-base. The embedding approach means the system understands a role described in unfamiliar wording, not just one that happens to share keywords with my profile. A job that never says “applied AI” but describes exactly that work still scores high.

    The Matching Interface and Feedback Loop

    The front end is a PWA served by a Worker, showing jobs ranked by ICP score — each with a three-sentence AI summary explaining why the role matches my profile, generated by Workers AI running LLaMA 3 8B over the job description and the ICP. When I rate a job with a thumbs up or down, that signal feeds back into the ICP score through a simple update, so the system gets sharper the more I use it. The feedback loop is deliberately lightweight; it doesn’t retrain anything, it just nudges the profile toward what I’ve actually shown interest in versus what only looked good on paper.

    The Real Numbers

    The system ingests 200 to 400 new jobs daily across all sources. Every morning it surfaces the top 12 matches through the PWA — a curated shortlist instead of a firehose. The processing cost is $0 per month, because every component lives inside a free tier and the daily volume never approaches the limits. A dozen well-ranked roles each morning, drawn from several hundred raw postings, is a fundamentally different experience than scrolling a job board for an hour, and it costs nothing to run.

    The Decision That Mattered Most

    The single best design decision was not trying to scrape LinkedIn. It would have been a constant fight against anti-scraping measures, and it would have pulled in noise from every direction. The job boards with open APIs — Greenhouse, Lever, Ashby — are used by the kinds of companies that build things with APIs. That is exactly the target company profile for my own search. The constraint became a filter: by limiting intake to companies that expose clean public endpoints, I automatically biased the whole pipeline toward technically serious organizations. The thing I chose not to build improved the results more than another feature would have.

    What Transfers

    The personal intelligence tool architecture generalizes to any domain where you’re sorting through high-volume, heterogeneous inputs to find relevant signals. Scheduled ingestion from multiple sources, embedding-based matching against a profile of what you care about, and a lightweight feedback loop that sharpens the profile over time — that pattern works for jobs, for monitoring news, for tracking research, for anything where the problem is too much input and not enough relevance. Build the profile, embed the inputs, rank by similarity, and let your own feedback tune it.

    → Building something at the intersection of AI, edge computing, and behavioral science? Let’s connect.

  • The Non-Linear Career Path: How Psychology + Finance Made Me a Better AI Engineer

    The question I get most often from people who look at my background is some version of: “How does a therapist end up doing AI work for defense contractors?” The answer is that the path wasn’t non-linear — it was compound. Each layer added something the previous layer couldn’t provide alone, and the combination produces a profile that’s genuinely unusual in AI deployment work.

    What the Therapy Training Actually Provided

    The Licensed Marriage and Family Therapist licensure isn’t a credential I hold as a curiosity. It came from graduate training in systems theory — understanding how behavior in one part of a system affects behavior in other parts — and from supervised clinical practice that required learning to hold complexity, ambiguity, and conflicting stakeholder interests simultaneously. Those skills transfer directly to AI deployment work, where the hardest problems are rarely technical. The hard problems are: why are the people this system is supposed to help resistant to using it, how do you design human-AI interaction so that people engage appropriately rather than over-trusting or under-trusting the outputs, and what happens to team dynamics when a model starts doing work that used to define someone’s role. A pure ML background doesn’t prepare you to navigate those questions. Clinical training does.

    What I/O Psychology Added

    Industrial-Organizational Psychology is the discipline that applies behavioral science to organizational contexts — selection, performance, motivation, team dynamics, organizational change. The master’s-level training in I/O psychology gave me a rigorous framework for thinking about how AI tools change work, not just how they perform tasks. When I was deploying automation in defense finance, the questions that mattered weren’t only “does the model work?” They were: how do we measure performance in a way that’s fair to the people whose work is changing, how do we design the transition so that the analysts whose time is being recovered feel like the beneficiaries rather than the displaced, and how do we structure human-AI collaboration so that human judgment is applied where it actually adds value rather than where it just feels necessary. I/O psychology has decades of research on exactly those questions.

    What Defense Finance Contributed

    Nineteen years in defense finance at Lockheed Martin provided two things that can’t be replicated by reading about them. The first is operational credibility: when I build an AI system for finance, I understand the workflows, the error consequences, the audit requirements, and the organizational dynamics of getting a finance team to trust a model. The second is a track record of consequential deployments: the major defense aircraft program withholds automation that produced $360K in annual cost avoidance and 2,132 recovered analyst hours per year, the internal tax scenario classifier that processed 415,000 tax scenarios in 4 hours and reduced purchase order error rates by 31%. Those outcomes exist because the technical work was grounded in deep domain knowledge.

    Why the Combination Is Rare

    Most AI engineers don’t have clinical training. Most clinicians don’t have 19 years in defense finance. Most finance professionals don’t have the technical depth to build production AI systems. The combination is rare not because it’s strategically constructed but because each step seemed like the obvious next thing given where I was. The therapy training came first and was about understanding people. The finance career built on that. The AI work emerged from the automation problems the finance work surfaced. The I/O psychology formalized frameworks I was already applying intuitively. In retrospect the path looks deliberate; at each step it was just following what was interesting and useful.

    What Transfers

    The argument this background makes isn’t that every AI engineer needs clinical training or that domain expertise always compounds into something useful. The argument is that AI deployment problems are fundamentally human problems — adoption, trust, organizational change, consequence management — and the people best positioned to solve them are those who have spent time on both the technical and the human sides. A pure-ML background is increasingly common. The combination is not.

    → Building something at the intersection of AI, edge computing, and behavioral science? Let’s connect.