Computational material scientist / DFT expert

Your mission

We are seeking an expert in computational materials science to help discover the next generation of breakthrough materials. You will work in a small, highly interdisciplinary team of DFT/AI/materials experts and develop and execute end-to-end workflows for identifying, ranking, and de-risking candidate materials.

You will collaborate closely with our synthesis and characterization team to translate predictions into experiments, improve models with real-world feedback, and build a closed-loop discovery engine. While there is an opportunity to publish selected results, our overarching goal is to deliver new, high-impact materials that can be validated experimentally and ultimately scaled.

This role combines scientific depth with teamwork and execution ownership. You will contribute to strategic decisions on methodology, prioritization, and platform direction. You will receive a competitive compensation package with possible equity participation.

Key responsibilities

Materials discovery & scientific leadership

• Design, implement, and continuously improve DFT-based workflows to predict key functional properties of crystalline materials.
• Develop screening strategies and decision criteria to down-select candidates from large search spaces and guide experimental validation.
• Own the computational plan for assigned discovery programs: define milestones, success criteria, and deliverables; drive execution.

Automation and experimental collaboration

• Build robust, automated, and reproducible DFT pipelines.
• Improve throughput via AI/ML workflow optimization.
• Maintain high-quality documentation, versioned workflows, and traceable datasets.
• Work with synthesis/characterization colleagues to interpret results, troubleshoot discrepancies, and incorporate learnings into improved workflows.

Communication & impact

• Communicate results clearly to both computational and experimental teammates (written summaries, internal presentations).
• Where appropriate, contribute to publications, conference abstracts, and grant/partner materials (without compromising IP strategy).

Your profile

Required qualifications

• PhD in Physics, Chemistry, Materials Science, or a closely related field.
• Deep hands-on experience in DFT on crystalline materials (4+ years), evidenced by strong publications and/or impactful open work.
• Demonstrated experience with automation and acceleration of DFT (workflows, convergence optimization, error handling, high-throughput screening).
• Experience applying AI/ML methods for materials discovery and search.
• Strong programming skills in Python (or similar) and familiarity with scientific software practices (version control, testing, reproducibility).
• Comfortable working in a fast-moving startup environment: pragmatic prioritization, ownership, strong collaboration.
• DFT + methods
• Experience with one or more of: VASP, Quantum ESPRESSO, GPAW, ABINIT, WIEN2k, CASTEP (or comparable codes).
• Familiarity with phonons, elastic properties, defect calculations, magnetism/spin-orbit coupling, or dielectric/ferroelectric calculations (depending on your target materials).
• Strong understanding of thermodynamics / phase stability and materials databases (e.g., Materials Project, OQMD, ICSD usage patterns).

Preferred qualifications

Workflow/HPC

• Hands-on HPC experience (Slurm, job scheduling, profiling, optimizing compute cost).
• Experience with workflow managers (ASE, AiiDA, atomate, FireWorks, custodian, Prefect, Airflow, Snakemake, Nextflow, etc.).
• Experience building/maintaining structured materials databases (Postgres, MongoDB, parquet/lakehouse patterns).

ML for materials

• Experience with materials representations (graph neural nets, CGCNN/MEGNet-like approaches, descriptors, symmetry-aware models).
• Knowledge of uncertainty quantification and decision-making under uncertainty for experimental planning.

Startup / industry

• Experience working with experimental teams or in an industrial R&D context.
• Track record translating predictions into validated materials and learning cycles.