Chemical Engineering AI Training Pod Lead

OpenTrain AI, Inc.; United States; Remote; USD 83,000 - 138,000; Part time

Job summary

OpenTrain AI, Inc. seeks a seasoned contractor to lead a pod of 5–10 technical trainers in creating and reviewing realistic chemical engineering tasks for AI training and evaluation.

You will ensure tasks reflect real workflows, reproducible results, and meaningful reasoning, covering reaction engineering to electrochemical engineering. Responsibilities include mentoring, evaluating problem statements, ensuring thermodynamic consistency, and guiding automated testing.

Qualifications

• Ph.D., postdoctoral experience, or equivalent advanced technical experience in Chemical Engineering.
• Strong ability in Python, Julia, C/C++, or MATLAB/Octave, plus proficiency in Linux environments.
• Knowledge in process modeling, reaction kinetics, thermodynamic modeling, process simulation, optimization, numerical methods, computational modeling, and technical validation.

Responsibilities

• Lead a pod of 5–10 technical trainers and ensure consistent quality and delivery standards.
• Review problem statements, reaction kinetics, thermodynamic and property data, and automated tests.
• Check mass and energy balances, thermodynamic consistency, units, convergence, tolerances, and initial conditions.
• Evaluate graders using outputs such as conversion, yield, selectivity, and energy use.
• Provide precise feedback, track revisions, and maintain technical documentation.

Skills

Chemical engineering expertise

Mentoring

Quality review

Problem assessment

Education

Ph.D./postdoctoral experience in Chemical Engineering

Tools

Cantera

CoolProp

Pyomo

IDAES

OpenFOAM

Docker

Job description

The work

You will lead a pod of about 5 to 10 technical trainers who create realistic, terminal-based chemical engineering tasks for AI training and evaluation. You will review whether each task reflects real engineering workflows, produces reproducible results, and tests meaningful reasoning.

The work covers reaction engineering, thermodynamics, phase equilibria, transport phenomena, separation processes, process control, optimization, combustion, and electrochemical engineering.

• Mentor technical trainers and maintain consistent quality and delivery standards.
• Review problem statements, reaction kinetics, thermodynamic and property data, process flowsheets, computational environments, reference solutions, and automated tests.
• Check mass and energy balances, thermodynamic consistency, units, 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 inaccuracies, unrealistic assumptions, edge cases, and weaknesses that could let calculations or simulations bypass the intended evaluation.
• Give precise feedback, track revisions, resolve quality issues, allocate work, monitor throughput, and maintain technical documentation.

What it pays and takes

This is a contractor assignment with a four-week duration. The role requires advanced chemical engineering experience and the ability to review complex technical work, simulations, computational models, and research outputs.

• Pay: Not provided in the listing.
• Time: 20+ hours per week; the assignment also requires at least four hours per day and four hours of overlap with Pacific Time.
• Location: Open worldwide.
• Language: English.
• Education or experience: Ph.D., postdoctoral experience, or equivalent advanced technical experience in Chemical Engineering.
• Programming: Strong ability in Python, Julia, C/C++, or MATLAB/Octave, plus proficiency in Linux environments.
• Technical knowledge: Process modeling, reaction kinetics, thermodynamic modeling, process simulation, optimization, numerical methods, computational modeling, and technical validation.
• Review skills: Ability to assess balances, thermodynamic assumptions, numerical accuracy, solver convergence, and computational reliability, then provide precise technical feedback.
• Leadership: Experience mentoring, reviewing, or leading small technical teams is valuable.
• Helpful tools and background: Cantera, CoolProp, Pyomo, IDAES, DWSIM, CasADi, OpenFOAM, Aspen Plus, Aspen HYSYS, gPROMS, Docker, Git, CI/CD, automated testing, AI coding agents, terminal-based agents, or LLM-generated engineering solutions. Experience in optimization, reactor modeling, separation systems, process control, electrochemical simulations, petrochemicals, pharmaceuticals, energy, specialty chemicals, or process manufacturing is also useful.

About AI training work

AI training work uses human-created examples, reviews, and evaluations to help artificial intelligence systems perform better. Experienced specialists are needed for technical projects because they can judge whether models, calculations, and answers are accurate and useful.