Chemical Engineering Training Pod Lead

Turing Enterprises, Inc.; Fatih; Remote; TRY 4,122,000 - 8,245,000; Full time

Benefits offered by this job

Contractor assignment (no medical/paid

Leave)

Job summary

Turing is seeking an experienced Chemical Engineering expert to lead a pod of 5–10 trainers creating realistic, terminal-based engineering tasks for an AI training initiative. You will oversee task accuracy, reproducibility, and evaluation quality while mentoring the team and maintaining high standards.

You will review problem statements, kinetics, thermodynamics, data, and simulations, ensuring meaningful multi-step reasoning and robust validation across mass/energy balances and solver

Qualifications

• Ph.D., postdoctoral experience, or equivalent advanced technical experience in Chemical Engineering.
• Strong programming skills in Python, Julia, C/C++, or MATLAB/Octave, with proficiency in Linux environments.
• Hands-on expertise in at least one major area: process modeling, reaction kinetics, thermodynamic modeling, process simulation, or optimization.
• Strong understanding 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 evaluate mass and energy balances, thermodynamic assumptions, numerical accuracy, solver convergence, and the reliability of computational results.
• Strong analytical judgment with 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 providing precise technical feedback and documenting quality standards.

Responsibilities

• Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of chemical engineering tasks.
• Review engineering tasks end to end, including problem statements, reaction kinetics, thermodynamic and property data, process flowsheets, computational environments, reference solutions, and automated tests.
• Validate tasks involving reaction engineering, thermodynamics, phase equilibria, transport phenomena, separation processes, process simulation, process control, optimization, combustion, and electrochemical engineering.
• Ensure tasks reflect realistic chemical engineering workflows and require meaningful multi-step technical reasoning.
• Verify engineering correctness across mass and energy balances, thermodynamic consistency, property data, unit handling, and numerical convergence.
• Validate computational models, stiff ODE/DAE solvers, solver tolerances, initial conditions, and simulation results to ensure reproducibility and technical accuracy.
• Review automated graders to ensure they evaluate meaningful engineering outputs such as conversion, yield, selectivity, purity, energy consumption, and process constraint satisfaction.
• Identify technical inaccuracies, unrealistic assumptions, incorrect tolerances, and opportunities to bypass engineering calculations or simulations.
• Provide clear, actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
• Onboard trainers, allocate work, monitor quality and throughput, elevate blockers, and maintain technical documentation and best practices.

Skills

Python

Julia

C/C++

MATLAB/Octave

Linux

Process modeling

Reaction kinetics

Thermodynamic modeling

Optimization

Education

Ph.D. in Chemical Engineering

Postdoctoral experience

Tools

Cantera

CoolProp

Pyomo

IDAES

DWSIM

CasADi

OpenFOAM

Docker

Git

Job description

Turing is seeking an experienced Chemical Engineering expert to lead a pod of 5–10 trainers creating realistic, terminal-based engineering tasks for an AI training initiative. You will oversee task accuracy, reproducibility, and evaluation quality while mentoring the team and maintaining high standards.

You will review problem statements, kinetics, thermodynamics, data, and simulations, ensuring meaningful multi-step reasoning and robust validation across mass/energy balances and solver