About this role.
Brain Co. seeks a part-time contractor advisor with hands-on experience submitting and shepherding building permit applications through approval. The consultant will test an AI-native permitting product using real project packages, identify errors in AI outputs, and explain jurisdiction-specific review practices. Ideal candidates are active or former homebuilders, architects of record, plans examiners, or permit engineers with deep knowledge of Florida, Texas, or California permitting. The engagement requires at least two hours weekly for three to four months, with possible extension. Technical AI expertise is not required; practical permitting judgment and candid product feedback are central to the work.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
4/5Pace & Pressure
3/5Autonomy Level
5/5Communication Load
5/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
A strong answer should outline the project type, submission documents, authority having jurisdiction, review timeline, comments received, revisions made, and the actions that ultimately secured approval. It should make clear that the candidate personally handled the package and reviewer interaction.
A strong answer should identify concrete issues such as incomplete drawings, inconsistent plans, missing calculations, code-interpretation errors, incomplete forms, local amendment conflicts, or insufficient site information. The candidate should distinguish between predictable administrative deficiencies and substantive code issues.
A strong answer should describe comparing the recommendation against the applicable code edition, local amendments, project facts, required documents, and actual jurisdictional review practice. The candidate should also explain how they would document uncertainty and flag recommendations needing professional or jurisdictional confirmation.
A strong answer should provide a specific example, such as preferred submission sequencing, reviewer expectations, recurring local documentation requirements, or effective ways to address comments. It should explain why that practice matters even if it is not obvious from published requirements.
A strong answer should be direct and evidence-based: identify the incorrect output, cite the relevant code, local amendment, or review practice, explain the likely consequence, and recommend the corrected behavior. The candidate should prioritize reproducible examples that product teams can use to improve the system.
Our Mission
Rebuild how the world works, to make institutions work better for the people they serve.
About Brain Co.
Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model.
Why Now
Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.
Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.
You’ll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you’ll still be proud of in ten years from now.
About the Role
Brain Co. is building the world’s first AI-native permitting, and we need people who’ve actually
done the work to help us get it right. You would play a role in helping shape AI Permitting in the US.
We’re looking for 1) homebuilders and developers – production builders, custom builders, small regionals, and 2) architects, and 3) engineers to join us as part-time advisors. You’ll work directly with our product and GTM teams, telling us where our AI falls short, what would actually change how you operate, and what it would take for a tool like this to earn a place in your workflow.
We are looking for someone for a minimum of 2 hours per week for 3-4 months. Opportunity to extend as needed.
What you’ll do
Walk us through how you submit – Tell us what is included, what you’ve learned to front-load, where submissions get tripped up
Use the product on a project – Run a real permit package through our software alongside normal process
Tell us where we are wrong – Review AI output on real permit submissions and flag errors, gaps, or misapplied code
Jurisdiction knowledge – Help us understand how review actually works across different jurisdictions and permit types
Help us learn what a good submissions looks like – Review sample packages and tell us what would sail through, come back with comments, or get rejected
Who we’re looking for
Someone who has personally submitted permits, not managed the process from a distance, but actually prepared packages, responded to comments, and tracked submissions through approval
Active or former plans examiner, permit engineer, architect of record, or homebuilder, anyone whose name is on the drawings or whose desk the rejection letter lands on
Deep knowledge of how permitting actually works in Florida, Texas, or California, the codes, the departments, the local amendments, and the unwritten rules
Enough history with a jurisdiction to tell us what reliably gets approved, what comes back with comments, and what gets rejected outright
Opinions about what’s broken and no hesitation sharing them – “this is wrong because…” is exactly what we need
Open to sharing examples on applications (passed and failed)
No technical background required – your job is to tell us if the AI gets it right, not to explain how it works
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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