Computational Chemistry Expert - PhD

Mercor; Greater London; On-site; GBP 55,000 - 75,000; Full time

Job summary

Mercor seeks a Computational Chemistry & Electronic Structure Expert to design graduate-level problems that test AI systems using real scientific software. You will craft challenging, multi-step workflows and evaluate AI performance on quantum chemistry tasks.

Ideal candidates hold MS or PhD-level training, have hands-on PySCF experience, strong Python skills, and can work independently in Linux/remote compute sandboxes. Availability: 15–20 hours weekly.

Qualifications

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent).
• Proven proficiency with at least one scientific software library (e.g., PySCF).
• Strong Python skills for problem setups, oracle functions, and validators.
• Ability to work independently and refine problem designs based on feedback.
• Comfortable working in a Linux/terminal environment with remote compute sandboxes.
• Available for at least 15–20 hours per week.

Responsibilities

• Design challenging problems requiring skilled use of specialized scientific software.
• Test problems against AI models and refine until target difficulty is reached.
• Create problems that require multi-step workflows and strategic queries.
• Interpret results and ensure issues from method limitations are exposed.
• Collaborate with the team to iterate on problem designs based on feedback.

Skills

Python

Linux terminal

Problem design

Research experience

Education

MS or PhD in a relevant STEM field

Tools

PySCF

Quantum chemistry libraries

Job description

Computational Chemistry & Electronic Structure Expert

About the Project

We're building a large-scale benchmark to test how well advanced AI systems can solve hard scientific and engineering problems. As a task designer, you'll create challenging computational problems that check whether AI can use real scientific software to do research-level work — running simulations, interpreting results, designing experiments, and uncovering hidden information from data.

This isn't a typical data-labeling job. You'll design original, graduate-level problems based on real scientific workflows, test them against cutting-edge AI models, and fine-tune them until the difficulty is just right.

What You'll Do

You'll create problems that require skilled use of specialized scientific software. Some will ask the AI to compute exact answers from a fully defined setup — testing whether it can correctly carry out complex, multi-step workflows. Others will be harder: the AI must plan a series of queries or experiments to uncover information that isn't directly visible, which means thinking strategically about what to measure, how to read partial results, and how to narrow down the possibilities efficiently.

Each problem goes through a testing loop against state-of-the-art AI models, and you'll refine it until it hits the target difficulty.

Domains & Tools We're Hiring For

We're especially interested in experts with deep, hands-on experience in:

Computational Chemistry & Electronic Structure

— working with PySCF for quantum chemistry calculations including Hartree-Fock, DFT, TDDFT, CASSCF, and post-HF methods. Ideal candidates can design problems around excited-state analysis, orbital diagnostics, choosing the right method for tricky electronic structures, and interpreting computational artifacts that come from method limitations.

Experience with other specialized software in this domain will also be considered.

What Makes a Strong Candidate

You have graduate-level expertise (MS or PhD preferred) in the domain above, with real hands-on experience using these tools — not just theoretical knowledge. You've written code using these libraries to solve actual research problems, and you understand where they break, what their edge cases are, and what makes a problem genuinely hard rather than just complicated.

Beyond domain expertise, the best candidates think like puzzle designers: building problems where the challenge comes from smart reasoning rather than raw computation, where several approaches seem plausible but only careful analysis reveals the right one, and where surface-level pattern matching won't get you to the answer.

Requirements

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience)

• Proven proficiency with at least one of the listed scientific software libraries, shown through research publications, open-source contributions, or professional work

• Strong Python skills — you'll be writing problem setups, oracle functions, and solution validators

• Ability to work independently and refine problem designs based on feedback

• Comfortable working in a Linux/terminal environment with remote compute sandboxes

• Available for at least 15–20 hours per week

Nice to Have

• Experience across multiple listed domains or tools

• Familiarity with benchmark or evaluation design

• Background in scientific teaching or exam/problem-set design

• Experience with computational reproducibility and containerized environments