{"id":3340822,"url":"https://alion.io/job/dayhoff-labs-research-scientist-quantum-chemistry","title":"Research Scientist, Quantum Chemistry","company":{"id":4403902,"name":"Dayhoff Labs","domain":"dayhofflabs.com","url":"https://alion.io/company/dayhoff-labs","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":6,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cambridge, United States","London, United Kingdom"],"countries":["US","GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":140000,"max_usd":311000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"HPC","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-07-05T22:55:43Z","employer_posted_date":"2026-07-05","last_verified_at":"2026-10-28T19:10:23Z","board_verified":true,"closed_at":null,"days_open":114,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":114},"description":"About us\nWe're reverse-engineering the origin of life - one of the great unsolved problems in science, and one we think AI finally makes tractable. Understanding this transition, from geochemistry to biochemistry, is what will let us orchestrate molecular networks and build systems that are more capable, adaptive, and efficient.\nIf we succeed, the applications are vast: catalysis, green synthesis, ab initio synthetic biology, programmable matter. Understanding and harnessing these processes could let ten billion of us thrive on this planet - and let life keep evolving beyond it.\nWe're a small, diverse team of AI engineers, computational scientists, and bench scientists. We hold ourselves to the rigor of a research institute, but we ship like an engineering firm. Global team, HQs in Cambridge, MA and London, UK.\nThe role\nYou'll turn quantum-chemistry calculations into kinetic datasets and mechanistic insight our ML models can actually train on. You'll study reaction mechanisms across homogeneous, heterogeneous, and enzymatic systems, and build the protocols that make that data reliable at scale.\nWhat you'll do\nRun DFT and post-HF calculations to study kinetics and mechanism, primarily in homogeneous catalysis\n\nBuild and benchmark reproducible protocols for kinetic data generation, with real uncertainty quantification\n\nDesign kinetic datasets for ML training and validation, and set data-quality standards with ML collaborators\n\nExtend these methods systematically across catalytic systems and reaction conditions\n\nEssential experience\nPhD in computational or theoretical chemistry with a catalysis focus, and first-author papers on catalytic mechanisms\n\nFluency with a production quantum-chemistry package (Gaussian, ORCA, or similar)\n\nSound DFT judgment for transition-metal systems: functional choice, basis sets, dispersion corrections\n\nHands-on kinetics: transition-state location, IRC, rate constants, free-energy and thermodynamic analysis\n\nPython and the computational-chemistry stack (ASE, cclib, RDKit)\n\nHighly preferred\nFirst-author work in homogeneous-catalysis kinetics\n\nHeterogeneous (periodic DFT, surfaces, adsorption) or enzyme catalysis\n\nAdvanced methods for hard systems: DLPNO-CCSD(T), CASPT2, multireference approaches\n\nHigh-throughput workflows, HPC, and automation\n\nUncertainty quantification and protocol benchmarking\n\nDataset design and prior collaboration with ML teams\n\nLogistics\nCompensation is highly competitive. 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