Aerodynamics Expert (MSc. or PhD)

Pay: $80 - $120/hour

Required Skills
Python
Aerodynamics
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.

Aerodynamics Expert (MSc. or PhD)

Pay: $80–$120/hour

Location: Global, fully remote

Job Type: Contractor (~15 hours per week)

Schedule: Flexible—you can choose the hours and days you work, including weekends if desired

We are looking for a highly skilled Aerodynamics Expert to contribute to an AI training project involving computational fluid dynamics, aerodynamic analysis, engineering simulation, and Python.

The work involves creating, solving, reviewing, and validating engineering tasks that can be completed through programmatic, command-line-based workflows. A representative task may require analyzing an airfoil, preparing a computational domain and mesh, defining appropriate boundary conditions, running a CFD simulation, calculating lift and drag, and determining whether the resulting flow field is numerically and physically valid.

What You’ll Work On
Solve and validate aerodynamic and fluid-dynamics problems.
Translate physical requirements into computational models and solver-ready cases.
Create or modify geometries, computational domains, and meshes.
Define appropriate initial conditions, boundary conditions, fluid properties, and solver settings.
Run CFD and aerodynamic-analysis workflows using command-line-accessible tools.
Use Python to automate preprocessing, simulation execution, parameter sweeps, postprocessing, and validation.

Required Qualifications

An MS or PhD in Aerospace Engineering, or Mechanical Engineering with an aerodynamics, fluid dynamics, or thermofluids specialization; or
A BS in Aerospace or Mechanical Engineering plus at least 1–2 years of aero-specific professional or research experience.
Strong understanding of fluid mechanics and applied aerodynamics.
Experience formulating and executing CFD or aerodynamic analyses.
Practical proficiency with
Python
.
Experience with at least one engineering tool or library that can be operated through a CLI, scripting interface, configuration files, or programmatic API.
Ability to select and justify modeling assumptions, boundary conditions, mesh strategies, and solver settings.
Ability to diagnose non-convergence, unstable solutions, incorrect results, and physically invalid outputs.
Ability to communicate technical reasoning and engineering trade-offs clearly.

Relevant tools may include Python, OpenFOAM, SU2, Gmsh, FEniCSx, Elmer, CalculiX, SciPy, or similar programmatic engineering software. Experience with an equivalent CLI-accessible tool is acceptable.

Relevant Python tools may include NumPy, SciPy, pandas, Matplotlib, Jupyter, solver APIs, mesh-processing libraries, or domain-specific scientific packages. No single library is mandatory.

Process
Apply to the role and complete the screening questions.
Complete an AI interview of approximately 30 minutes.
Complete the hiring manager review.
Compensation Structure

Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.

Minimum submission requirements apply.

Start Timeline & Availability
We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.
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