The work
You will guide a pod of about 5 to 10 technical trainers who create realistic terminal-based chemical engineering tasks for AI training and evaluation. The work covers reaction engineering, thermodynamics, phase equilibria, transport phenomena, separations, process control, optimization, combustion, and electrochemical engineering.
You will review engineering problems, computational models, process simulations, reference solutions, and automated graders. You will check whether each task reflects real engineering workflows, produces reproducible results, and tests meaningful technical reasoning.
- Review reaction kinetics, thermodynamic and property data, process flowsheets, computational environments, reference solutions, and automated tests.
- Check mass and energy balances, thermodynamic consistency, unit handling, numerical convergence, solver tolerances, initial conditions, and simulation results.
- Evaluate graders using outputs such as conversion, yield, selectivity, purity, energy consumption, and process constraints.
- Find technical errors, unrealistic assumptions, edge cases, and weaknesses that could let calculations or simulations be bypassed.
- Mentor trainers, assign work, monitor throughput, track revisions, resolve quality issues, provide precise feedback, and maintain technical documentation.
What it pays and takes
This is a contractor assignment lasting four weeks. The work requires sustained availability and overlap with Pacific Time.
- At least 40 hours per week, including at least four hours per day.
- At least four hours of daily overlap with Pacific Time.
- Open worldwide, with English fluency needed for technical review, documentation, and feedback.
- A Ph.D., postdoctoral experience, or equivalent advanced technical experience in Chemical Engineering.
- Strong programming ability in Python, Julia, C/C++, or MATLAB/Octave.
- Proficiency in Linux environments.
- Expertise in process modeling, reaction kinetics, thermodynamic modeling, process simulation, optimization, or a closely related area.
- Strong knowledge of chemical engineering fundamentals, numerical methods, computational modeling, and technical validation.
- Experience reviewing complex technical work, chemical process simulations, computational models, or research outputs.
- Ability to assess balances, thermodynamic assumptions, numerical accuracy, solver convergence, and computational reliability.
- Experience mentoring, reviewing, or leading small technical teams is valuable.
- Helpful tools and background include Cantera, CoolProp, Pyomo, IDAES, DWSIM, CasADi, OpenFOAM, Aspen Plus, Aspen HYSYS, gPROMS, Docker, Git, CI/CD, automated testing, AI coding agents, and terminal-based agents.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training is the human work behind systems that learn from examples, including reviewing code, simulations, and model outputs. OpenTrain is a platform where people find AI training and data-labeling projects, build a profile, and apply in minutes.