Remote Pod Lead - Chemical Engineering

Turing, a rapidly growing AI company, is advancing the development and deployment of cutting-edge AI systems. The company collaborates with leading AI labs to push the boundaries of model capabilities in areas such as reasoning, coding, multimodality, and STEM knowledge. These advancements are then applied to create real-world AI solutions that address critical business challenges.

We are looking for an experienced Chemical Engineering professional to lead a team of technical trainers focused on creating realistic, terminal-based engineering tasks for a frontier AI training and evaluation initiative. In this role, you will manage a pod of 5 to 10 trainers, ensuring that chemical engineering tasks are technically accurate, reproducible, and of high evaluation quality. You will also mentor trainers, uphold quality standards, and drive consistent delivery of tasks that mirror authentic chemical engineering workflows.

Key Responsibilities:
- Lead and mentor a team of technical trainers, ensuring high-quality, productive, and timely delivery of chemical engineering tasks.
- Review tasks comprehensively, 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 require meaningful multi-step technical reasoning and reflect realistic chemical engineering workflows.
- 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 confirm they evaluate relevant engineering outputs such as conversion, yield, selectivity, purity, energy consumption, and process constraint satisfaction.
- Identify technical inaccuracies, unrealistic assumptions, incorrect tolerances, and potential bypasses of engineering calculations or simulations.
- Provide clear, actionable technical feedback, track revisions, and ensure resolution of quality issues before task approval.
- Onboard trainers, allocate work, monitor quality and throughput, escalate blockers, and maintain technical documentation and best practices.

Required Qualifications:
- Ph.D., postdoctoral experience, or equivalent advanced technical expertise 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 such as process modeling, reaction kinetics, thermodynamic modeling, process simulation, or optimization.
- Deep 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 critically evaluate mass and energy balances, thermodynamic assumptions, numerical accuracy, solver convergence, and computational result reliability.
- Strong analytical judgment to identify technical errors, edge cases, and flawed engineering assumptions.
- Experience mentoring, reviewing, or leading small technical teams.
- Excellent written communication skills for delivering precise technical feedback and documenting quality standards.

Preferred Qualifications and Benefits:
- Familiarity with chemical engineering simulation and computational tools such as Cantera, CoolProp, Pyomo, IDAES, DWSIM, CasADi, or OpenFOAM.
- Experience with commercial process simulation software like Aspen Plus, Aspen HYSYS, or gPROMS, including replicating workflows with open-source tools.
- Background in process optimization, reactor modeling, separation systems, process control, or electrochemical simulations.
- Knowledge of 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 involvement in AI training, technical quality control, benchmark development, or evaluation programs.

Offer Details:
- Commitment of at least 4 hours per day and a minimum of 40 hours per week, with a 4-hour overlap with PST.
- Contractor assignment with no medical or paid leave benefits.
- Contract duration of 4 weeks, with an expected start date as early as next week.