{"id":3340823,"url":"https://alion.io/job/dayhoff-labs-research-engineer-accelerated-quantum-chemistry","title":"Research Engineer, Accelerated 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":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","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":135000,"max_usd":260000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":508},"experience_years_min":3,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Docker","optional":false},{"name":"HPC","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SciPy","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-07-05T22:43:48Z","employer_posted_date":"2026-07-05","last_verified_at":"2026-10-30T01:13:58Z","board_verified":true,"closed_at":null,"days_open":116,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":115},"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 build simulation pipelines that fuse conventional computational chemistry with AI-accelerated models, in a setting where the simulation and the experiment are on the same clock. Build it, deploy it, watch it get tested - often in the same month.\nWhat you'll do\nRun QM/MD simulations combining standard packages with AI-accelerated models\n\nBuild reproducible pipelines and benchmarking protocols across QM, MD, and ML\n\nDeploy simulation tools for internal teams; work with software and product on external deployment\n\nIntegrate neural network potentials into traditional QM/MD workflows with the ML team\n\nEssential experience\nPhD in computational chemistry, chemical physics, materials science, or related field - or a Master's with 3+ years relevant experience\n\nHands-on experience with QM and MD packages (e.g., VASP, Gaussian, ORCA, GROMACS, LAMMPS, CP2K)\n\nTrack record of building computational pipelines and reproducible workflows\n\nProficiency in Python and scientific computing libraries (NumPy, SciPy, computational chemistry libraries)\n\nExperience with ML frameworks (PyTorch, TensorFlow) and their integration into computational chemistry workflows\n\nHighly preferred\nNeural network potentials and modern AI models for molecular simulation (e.g., graph neural networks, transformer models)\n\nHPC environments and workflow management systems\n\nContainerization (Docker) and deployment pipelines\n\nBenchmarking and statistical validation of computational methods\n\nTranslating computational insights into practical applications\n\nLogistics\nCompensation is highly competitive. 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