Remote Pod Lead - Chemical Engineering

Turing

1d•Remote

About The Position

Turing is seeking an experienced Chemical Engineering expert to lead a pod of approximately 5–10 technical trainers developing realistic, terminal-based engineering tasks for a frontier AI training and evaluation initiative. The Pod Lead will oversee task quality, technical accuracy, reproducibility, evaluation standards, and timely delivery, while mentoring trainers and ensuring tasks reflect authentic chemical engineering workflows. The role includes end-to-end review of engineering tasks, covering problem statements, reaction kinetics, thermodynamic and property data, process flowsheets, computational environments, reference solutions, and automated tests. Tasks may cover reaction engineering, thermodynamics, phase equilibria, transport phenomena, separation processes, process simulation and control, optimization, combustion, and electrochemical engineering. The role also involves checking engineering calculations and computational models, validating graders, giving actionable feedback, onboarding trainers, allocating work, monitoring quality and throughput, escalating blockers, and maintaining technical documentation and best practices. The assignment requires at least 4 hours per day and a minimum of 40 hours per week, with 4 hours of overlap with PST. It is a four-week contractor assignment with no medical or paid leave; the expected start date is next week.

Requirements

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 and 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.
Commitment of at least 4 hours per day and a minimum of 40 hours per week, with 4 hours of overlap with PST.

Nice To Haves

Experience with chemical engineering simulation and computational tools such as Cantera, CoolProp, Pyomo, IDAES, DWSIM, CasADi, or OpenFOAM.
Familiarity with commercial process simulation software such as Aspen Plus, Aspen HYSYS, or gPROMS, including reproducing workflows using open-source tools.
Experience with process optimization, reactor modeling, separation systems, process control, or electrochemical simulations.
Familiarity with Docker, Git, CI/CD pipelines, and automated testing.
Experience developing or evaluating AI coding agents, terminal-based agents, or LLM-generated engineering solutions.
Industry experience in petrochemicals, pharmaceuticals, energy, specialty chemicals, or chemical process manufacturing.
Prior experience in AI training, technical quality control, benchmark development, or evaluation programs.

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 for reproducibility and technical accuracy.
Review automated graders to ensure they evaluate meaningful engineering outputs, including 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, escalate blockers, and maintain technical documentation and best practices.