Pay: $85 - $185/hour
Required Skills
Technical Depth in a Subdomain
Engineering Judgment Under Constraints
Knowing When an Answer Is Wrong
Aerodynamics
Propulsion
Structures
About micro1
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.
Role Title: Aerospace Engineering Expert
Role Type: Contractor
Location: Remote
Opportunity Snapshot
Pay: $80- $185/hour
Commitment: Flexible — fits alongside your current role
Location: Fully remote
You’ll need: Master’s or PhD in aerospace or a closely related field, plus principal-level industry experience or a faculty appointment
Your tasks: Build challenging technical problems from your own domain and define what makes an answer correct
You'll be working on
The work draws on what you already know: the problems where the textbook answer isn’t enough, where the right approach depends on reading the constraints correctly, where a plausible-looking solution is quietly wrong. You’ll build those problems, provide the correct analysis, and define what separates a right answer from one that only looks right.
What you’ll do
Design difficult technical problems from your own area — aerodynamics, propulsion, structures, GNC, orbital mechanics, systems engineering — with realistic constraints and supporting data.
Provide the correct solution and walk through the reasoning that gets there.
Define objective criteria for correctness, so another expert reviewing the work would reach the same verdict.
Review problems built by other contributors for technical accuracy and soundness of method.
Flag problems that turn out to be ambiguous or ungradable rather than submitting them.
What we’re looking for
Master’s or PhD in aerospace engineering, mechanical engineering, or a closely related field.
Principal, staff, or fellow-level experience at a major aerospace organization, or a faculty appointment at a leading university.
Deep technical depth in at least one aerospace subdomain, with hands-on ownership of the work.
Able to explain why an answer is correct clearly enough that another engineer could verify it.
Publications, patents, or a record of technical leadership is a strong plus.
AI or LLM evaluation experience is a plus, but not required.
How the selection process works
Apply and complete the screening questions
Complete the AI interview for this role — it’s how your expertise gets in front of the client
Profile review
Onboarding — our Talent Ops team will get you set up
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