Every AI training role, research fellowship, and science opportunity has a shape — a set of conditions that must align for it to exist. SciReach uses inverse design reasoning to identify exactly the roles that fit your expertise, before they close.
Tell SciReach the role type, rate, expertise domain, and availability window. It treats this as a constraint specification — a set of boundary conditions the ideal opportunity must satisfy.
Instead of scrolling listings forward, SciReach inverts the search — asking what conditions would produce a role matching your constraints, then finding the exact opportunities that satisfy them.
Identifies the top matches across Mindrift, Appen, Labelbox, and other platforms. Fills assessments, crafts tailored responses, and submits applications — on your behalf, while you focus on the science.
The AI training market pays domain experts — chemists, physicists, clinicians, engineers — $40–100/hr to train LLMs. But the opportunities are scattered, the applications are manual, and the assessments take hours to complete. High-skill researchers spend their evenings doing data entry.
The tools that exist for automating job applications were built for recruiters and engineers. Nobody built one for scientists who want to monetize expertise without becoming a bureaucrat.
Mindrift, Appen, Scale AI — specialist roles for credentialed experts
NIST, NSF, DOE — competitive programs with complex application requirements
Python engineers and scientists needed to test and refine AI agent reasoning
AI companies hiring PhDs for red-teaming, evaluation, and content review
Most job-matching tools work forward — they index listings and match keywords. SciReach works backward from your desired outcome: the role type, compensation range, expertise area, and availability. It identifies the constraints that would produce an ideal opportunity, then finds roles that satisfy them.
This is the same logic behind AlphaEvolve, dZiner, and the systems discovering new materials by reasoning backward from properties to structure. Applied to opportunity discovery, it means better matches — not more volume.
"Most tools find what's easy to find. We find what should exist — and find it first."
Built for scientists and domain experts who understand that opportunity discovery is a constraint-satisfaction problem — and that the right agent can solve it faster than any manual search.
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