Materials & Semiconductor Physics Technical Lead

About Turing & Role

Turing is one of the world's leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent. We are staffing a frontier AI data initiative building the infrastructure and training data used to develop and evaluate AI agents. You will be assigned to one of two tracks: connectors or tasks.

Role Overview 

We are seeking an experienced Materials Science or Semiconductor Physics Expert to evaluate and benchmark advanced AI models against real-world materials discovery workflows. Working directly with client R&D teams, you will turn atomic-scale engineering challenges—spanning thin-film deposition (ALD, CVD, PVD), etch, planarization, and multiscale physics simulations (DFT, MD, kMC)—into structured test problems, define scientific grading criteria, and diagnose why frontier AI models succeed or fail.   

Key Responsibilities: 

• Support technical discovery sessions with client’s R&D teams to map and deconstruct the end-to-end materials discovery lifecycle into discrete sub-processes. 
• Convert real materials engineering challenges into structured test scenarios with defined inputs, constraints, and verified golden reference solutions. 
• Define clear scoring rubrics and programmatic validation rules (e.g., verifying stoichiometry, thermodynamics, and non-diverging simulation parameters). 
• Inspect step-by-step AI reasoning traces to diagnose failure root causes. 
• Scope out domain-specific data generation requirements, synthetic physics pipelines, and fine-tuning strategies to advance AI model performance.  

Qualifications:

• PhD or Master's degree in Materials Science, Applied Physics, Chemical Engineering, Microelectronics, or a related discipline. 
• Deep technical expertise in atomic-scale materials engineering: precision thin-film deposition (ALD, CVD, PVD), plasma etch, CMP, 3D packaging, or advanced semiconductor memory/logic architectures. 
• Hands-on familiarity with computational physics/chemistry modeling pipelines: Density Functional Theory (DFT), Molecular Dynamics (MD), or Kinetic Monte Carlo (kMC) simulation setups.  
• Proven ability to formulate complex, open-ended scientific workflows into structured, verifiable problem statements with clear ground-truth criteria. 
• Strong verbal & written communication skills required during technical and business workshops.

Benefits

• Opportunity to work on cutting-edge AI projects.
• Competitive compensation.
• Flexible working hours and remote work environment.

Application Process

• Shortlisted candidates will need to go through online interviews.
• Once the interview is cleared, candidates can start.