[Hiring] Pod Lead - Material Science @Turing

Role Description

We are seeking an experienced Materials Science expert to lead a team of technical trainers developing realistic, terminal-based scientific tasks for a frontier AI training and evaluation initiative.

• Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of materials science tasks.
• Review scientific tasks end to end, including:
  • Problem statements
  • Crystal structures
  • Interatomic potentials
  • Thermodynamic databases
  • Simulation inputs
  • Reference solutions
  • Automated tests
• Validate tasks involving:
  • Density functional theory (DFT)
  • Molecular dynamics
  • Computational thermodynamics
  • Phase diagrams
  • Crystallography
  • Materials characterization
  • Mechanical properties
  • Semiconductors
  • Energy materials
• Ensure tasks reflect realistic materials research workflows and require meaningful multi-step scientific reasoning rather than simple database lookups or library calls.
• Verify scientific correctness across:
  • Units
  • Crystal symmetry
  • Convergence criteria
  • Electronic structure calculations
  • Thermodynamic consistency
  • Simulation parameters
• Validate computational environments, scientific software, pseudopotentials, interatomic potentials, and dependencies to ensure reproducible results.
• Review automated graders to ensure they evaluate meaningful scientific outputs such as:
  • Lattice parameters
  • Formation energies
  • Band gaps
  • Elastic constants
  • Phonon properties
  • Phase fractions
  • Diffusion coefficients
  • Microstructure metrics
• Identify technical inaccuracies, unrealistic assumptions, incorrect numerical tolerances, and opportunities to bypass scientific 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.

Qualifications

• Ph.D., postdoctoral experience, or equivalent advanced technical experience in Materials Science, Metallurgy, or a closely related discipline.
• Strong programming skills in Python, C/C++, Fortran, or Julia, with proficiency in Linux and terminal-based environments.
• Hands-on expertise in at least one major area: atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
• Strong understanding of materials science fundamentals, numerical methods, computational modeling, and scientific validation.
• Experience reviewing complex technical work, scientific code, computational simulations, manuscripts, or research outputs.
• Ability to evaluate scientific assumptions, numerical accuracy, simulation convergence, and the reliability of computational results.
• Strong analytical judgment with the ability to identify technical errors, edge cases, and flawed scientific assumptions.
• Experience mentoring, reviewing, or leading small technical or research teams.
• Excellent written communication skills for providing precise technical feedback and documenting quality standards.

Nice to have

• Experience with computational materials science tools such as Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, or DREAM3D.
• Experience with machine-learned interatomic potentials, high-throughput materials simulations, or materials databases such as Materials Project.
• Familiarity with Docker, Git, CI/CD pipelines, automated testing, and high-performance computing (HPC) environments.
• Experience developing scientific benchmarks, programming challenges, simulation-based evaluations, or automated grading systems.
• Experience developing or evaluating AI coding agents, terminal-based agents, or LLM-generated scientific solutions.
• Industry experience in semiconductors, batteries, alloys, advanced materials, or manufacturing.
• Publications, research contributions, or open-source development experience in computational materials science.
• Prior experience in AI training, scientific 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 weeks [expected start date is next week]