Mechanical & Aerospace Engineering Task Lead (AI Project)

Gramian Consulting Group; Brasil; Remote; BRL 421,000 - 701,000; Full time

Job summary

Gramian Consultancy is seeking an experienced Mechanical or Aerospace Engineering Expert to lead a team of 5–10 technical trainers, developing realistic, terminal-based tasks for advanced AI training. You will oversee task quality, review engineering content, and ensure graders evaluate meaningful outcomes.

PhD, postdoctoral experience, or equivalent advanced expertise is mandatory. This contractor role runs 4 weeks, hourly up to 40h/week, with remote locations including Brazil and other

Qualifications

• PhD, postdoctoral experience, or equivalent advanced technical expertise in Mechanical Engineering, Aerospace Engineering, or a closely related discipline.
• Strong programming skills in Python, C/C++, Fortran, Julia, or MATLAB/Octave.
• Proficiency in Linux and command-line environments.

Responsibilities

• Lead and mentor a team of 5–10 technical trainers, ensuring consistent task quality, productivity, and timely delivery.
• Review engineering tasks end to end, including problem statements, geometry and mesh files, simulation inputs, material properties, reference solutions, and automated tests.
• Validate tasks involving FEA, CFD, thermodynamics, heat transfer, dynamics, robotics, flight simulation, propulsion modeling, and orbital mechanics.
• Ensure tasks reflect realistic mechanical and aerospace engineering workflows and require meaningful, multi-step technical reasoning.
• Verify engineering correctness across units, equilibrium, conservation laws, boundary conditions, mesh convergence, solver tolerances, reference frames, and numerical stability.
• Validate computational environments, simulation tools, solvers, and dependencies to ensure reproducible results.
• Review automated graders to ensure they assess meaningful engineering outputs, including stresses, deflections, natural frequencies, aerodynamic coefficients, thermal performance, orbital parameters, and safety margins.
• Identify technical inaccuracies, unrealistic assumptions, incorrect tolerances, and opportunities to bypass genuine engineering calculations or simulations.
• Provide actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
• Onboard trainers, allocate work, monitor quality and throughput, resolve technical blockers, and maintain technical documentation and best practices.

Skills

Python

C/C++

Fortran

Julia

MATLAB/Octave

Linux CLI

Analytical thinking

FEA/CDP expertise

Education

PhD or equivalent

Tools

FEA software

CFD tools

Job description

About Gramian

Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.

About the Role

We are seeking an experienced Mechanical or Aerospace Engineering Expertto lead a team of technical trainers developing realistic, terminal-based engineering tasks for advanced AI training and evaluation. You will oversee a pod of approximately 5–10 trainers, review tasks for engineering accuracy and reproducibility, and ensure automated graders reliably assess technical outcomes. This role combines advanced engineering expertise, computational modeling, technical quality assurance, and team leadership to help evaluate and improve next-generation AI systems.

CONTRACT:

Contractor assignment, 4 weeks

COMMITMENT:

Hourly, up to 40h/week

LOCATIONS:

Remote - Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Turkey, Vietnam

PROCESS:

AI Interview

NOTES: PhD Degree is mandatory!

Key Responsibilities

• Lead and mentor a team of 5–10 technical trainers, ensuring consistent task quality, productivity, and timely delivery.
• Review engineering tasks end to end, including problem statements, geometry and mesh files, simulation inputs, material properties, reference solutions, and automated tests.
• Validate tasks involving finite-element analysis (FEA), computational fluid dynamics (CFD), thermodynamics, heat transfer, dynamics, robotics, flight simulation, propulsion, and orbital mechanics.
• Ensure tasks reflect realistic mechanical and aerospace engineering workflows and require meaningful, multi-step technical reasoning.
• Verify engineering correctness across units, equilibrium, conservation laws, boundary conditions, mesh convergence, solver tolerances, reference frames, and numerical stability.
• Validate computational environments, simulation tools, solvers, and dependencies to ensure reproducible results.
• Review automated graders to ensure they assess meaningful engineering outputs, including stresses, deflections, natural frequencies, aerodynamic coefficients, thermal performance, orbital parameters, and safety margins.
• Identify technical inaccuracies, unrealistic assumptions, incorrect tolerances, and opportunities to bypass genuine engineering calculations or simulations.
• Provide actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
• Onboard trainers, allocate work, monitor quality and throughput, resolve technical blockers, and maintain technical documentation and best practices.

• PhD, postdoctoral experience, or equivalent advanced technical expertise in Mechanical Engineering, Aerospace Engineering, or a closely related discipline.

• Strong programming skills in Python, C/C++, Fortran, Julia, or MATLAB/Octave.

• Proficiency in Linux and command-line environments.

• Hands-on expertise in at least one major area, such as FEA, CFD, dynamics, flight simulation, thermal modeling, astrodynamics, guidance, navigation and control (GNC), or propulsion modeling.

• Strong understanding of engineering simulation, numerical methods, computational modeling, and technical validation.

• Experience reviewing complex technical work, engineering simulations, computational models, or research outputs.

• Ability to assess engineering assumptions, numerical accuracy, solver convergence, and computational reliability.

• Strong analytical judgment and the ability to identify technical errors, edge cases, and flawed engineering assumptions.

• Experience mentoring, reviewing, or leading small technical teams.

• Excellent written communication skills for precise technical feedback and documentation.