Particle Physics Expert - Computational

Mercor; Paris; Sur place; EUR 55 000 - 83 000; Temps partiel

Résumé du poste

Mercor is seeking a Particle & Nuclear Physics Expert to join a project building a large-scale benchmark to test AI systems on challenging scientific tasks in Paris. You will design graduate-level problems using real scientific software, run simulations, interpret results, and design experiments to reveal AI capabilities.

You’ll work with scikit-hep and related HEP Python tools, crafting problem setups, validators, and testing loops.

Qualifications

• Graduate-level training in a STEM field (MS/PhD or equivalent).
• Proven proficiency with scientific software libraries, publications, or open-source work.
• Strong Python skills for problem setups, oracle functions, and validators.
• Ability to work independently and refine problem designs based on feedback.
• Comfort with Linux/terminal environments and remote compute sandboxes.
• Available for at least 15–20 hours per week.

Responsabilités

• Design original graduate-level problems using real scientific workflows.
• Run simulations, interpret results, and design experiments.
• Test problems against state-of-the-art AI models and refine difficulty.
• Collaborate on domains and tools in Particle & Nuclear Physics.

Connaissances

Python programming

Independent work

Problem design thinking

Graduate-level STEM training

Formation

MS or PhD in a relevant STEM field

Outils

scikit-hep

HEP Python tools

Monte Carlo methods

Collider phenomenology

Linux/Unix environments

Description du poste

Particle & Nuclear Physics 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:

Particle & Nuclear Physics — working with scikit-hep and related HEP Python tools for particle physics data analysis, cross-section computations, renormalization group calculations, and perturbative QCD. Experience with Monte Carlo event generation or collider phenomenology is a plus.

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