Pod lead - Chemical Engineering (Terminal Bench Science)

About Turing: Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies. Role Overview: We are seeking an experienced Chemical Engineering expert to lead a team of technical trainers developing realistic, terminal-based engineering tasks for a frontier AI training and evaluation initiative. In this role, you will oversee a pod of approximately 5–10 trainers, review chemical engineering tasks for technical accuracy, reproducibility, and evaluation quality, and ensure tasks reflect authentic chemical engineering workflows. You will also mentor trainers, maintain quality standards, and drive consistent task delivery. What you'll do: 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, escalate blockers, and maintain technical documentation and best practices. What we're looking for: 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. Nice to have: 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. Offer Details: Commitments Required: At least 4 hours per day and minimum 40 hours per week with overlap of 4 hours with PST. Employment type : Contractor assignment (no medical/paid leave) Duration of contract : 4 week [expected start date is next week]