{"id":1312020,"url":"https://alion.io/job/this-computational-material-scientist-dft-expert","title":"Computational material scientist / DFT expert","company":{"id":3786870,"name":"this","domain":"alqem.ai","url":"https://alion.io/company/alqem","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Personio","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Science & Research","role_family":"Science & Research","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Munich, Germany"],"countries":["DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":75000,"max_usd":142000,"period":"year","method":"role_seniority_country_cell","sample_n":10},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Python","optional":false},{"name":"HPC","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prefect","optional":true},{"name":"SLURM","optional":true}],"status":"live","first_seen_at":"2026-02-24T12:53:57Z","employer_posted_date":"2026-02-24","last_verified_at":"2026-10-28T23:46:24Z","board_verified":true,"closed_at":null,"days_open":246,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":246},"description":"Your mission\nWe 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.\nThis 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.\nKey responsibilities\nMaterials discovery & scientific leadership\nDesign, implement, and continuously improve DFT-based workflows to predict key functional properties of crystalline materials.\nDevelop screening strategies and decision criteria to down-select candidates from large search spaces and guide experimental validation.\nOwn the computational plan for assigned discovery programs: define milestones, success criteria, and deliverables; drive execution. \nAutomation and experimental collaborationBuild robust, automated, and reproducible DFT pipelines.\nImprove throughput via AI/ML workflow optimization.\nMaintain high-quality documentation, versioned workflows, and traceable datasets.\nWork with synthesis/characterization colleagues to interpret results, troubleshoot discrepancies, and incorporate learnings into improved workflows. \nCommunication & impactCommunicate results clearly to both computational and experimental teammates (written summaries, internal presentations).\nWhere appropriate, contribute to publications, conference abstracts, and grant/partner materials (without compromising IP strategy).\nYour profile\nRequired qualificationsPhD in Physics, Chemistry, Materials Science, or a closely related field.\nDeep hands-on experience in DFT on crystalline materials (4+ years), evidenced by strong publications and/or impactful open work.\nDemonstrated experience with automation and acceleration of DFT (workflows, convergence optimization, error handling, high-throughput screening).\nExperience applying AI/ML methods for materials discovery and search.\nStrong programming skills in Python (or similar) and familiarity with scientific software practices (version control, testing, reproducibility).\nComfortable working in a fast-moving startup environment: pragmatic prioritization, ownership, strong collaboration.\nDFT + methods\nExperience with one or more of: VASP, Quantum ESPRESSO, GPAW, ABINIT, WIEN2k, CASTEP (or comparable codes).\nFamiliarity with phonons, elastic properties, defect calculations, magnetism/spin-orbit coupling, or dielectric/ferroelectric calculations (depending on your target materials).\nStrong understanding of thermodynamics / phase stability and materials databases (e.g., Materials Project, OQMD, ICSD usage patterns).\nPreferred qualificationsWorkflow/HPC\nHands-on HPC experience (Slurm, job scheduling, profiling, optimizing compute cost).\nExperience with workflow managers (ASE, AiiDA, atomate, FireWorks, custodian, Prefect, Airflow, Snakemake, Nextflow, etc.).\nExperience building/maintaining structured materials databases (Postgres, MongoDB, parquet/lakehouse patterns). \nML for materialsExperience with materials representations (graph neural nets, CGCNN/MEGNet-like approaches, descriptors, symmetry-aware models).\nKnowledge of uncertainty quantification and decision-making under uncertainty for experimental planning. \nStartup / industryExperience working with experimental teams or in an industrial R&D context.\nTrack record translating predictions into validated materials and learning cycles.","description_format":"text","description_chars":4029,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-26T17:00:25Z"}],"visa":[],"liveness":{"score":6,"band":"cold","label":"Long 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