{"id":2388631,"url":"https://alion.io/job/monarchlabs-research-scientist-computational-chemistry","title":"Research Scientist - Computational Chemistry","company":{"id":4417470,"name":"Monarch","domain":"monarchlabs.org","url":"https://alion.io/company/monarchlabs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","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":"2026-10-22T06:10:10Z"}},"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":["Emeryville, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":149000,"max_usd":329000,"period":"year","method":"role_country_seniority_unknown","sample_n":4194},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2025-01-24T20:42:35Z","employer_posted_date":"2025-01-24","last_verified_at":"2026-10-23T01:49:44Z","board_verified":true,"closed_at":null,"days_open":636,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":635},"description":"We offer opportunities to do your life’s work while helping solve one of the most important technical and moral challenges of our time.\nFull-time, in-office in Emeryville, California. Compensation includes equity.\nOur mosquito work\nBuild the computational chemistry layer that connects molecular structure, physicochemical properties, formulation context, and observed mosquito behavior. Your work should help us choose more informative compounds to test, not merely explain results after the fact.\nKey Responsibilities\n\nDevelop molecular representations and predictive models for compound effects on mosquito landing and related behavioral endpoints\n\nCombine chemical structures and descriptors with formulation, dose, assay, environmental, and behavioral data\n\nDesign prospective evaluations that measure whether model-ranked compounds outperform conventional selection approaches\n\nQuantify uncertainty, identify out-of-domain predictions, and propose experiments that distinguish competing chemical hypotheses\n\nPartner with formulation chemists and machine-learning researchers to recommend the next compound, dose, formulation, or controlled variant to test\n\nBuild reproducible computational workflows with traceable structures, descriptors, model versions, and experimental outcomes\nQualifications\n\nPh.D. or equivalent research experience in computational chemistry, chemoinformatics, physical chemistry, medicinal chemistry, chemical engineering, or a related field\n\nExperience with molecular descriptors, similarity methods, QSAR, molecular machine learning, or graph-based models\n\nStrong Python skills and experience with tools such as RDKit or equivalent chemical-computing libraries\n\nAbility to design leakage-resistant evaluations and interpret model performance in chemical rather than purely statistical terms\n\nClear scientific writing and close collaboration with experimental teams\nDesired Attributes\n\nExperience with active learning, Bayesian optimization, uncertainty calibration, or prospective molecular discovery\n\nKnowledge of volatility, solubility, controlled release, odorants, or insect-active small molecules\n\nExperience connecting computation to iterative wet-lab experiments\n\nInterest in building open, reusable scientific methods rather than a one-time screening model\nOur crop-protection work\nBuild the computational chemistry layer that connects molecular structure, physicochemical properties, formulation context, and observed insect behavior. Your work should help us choose more informative compounds to test, not merely explain results after the fact.\nKey Responsibilities\n\nDevelop molecular representations and predictive models for compound effects on insect landing on crops and related behavioral endpoints\n\nCombine chemical structures and descriptors with formulation, dose, assay, environmental, and behavioral data\n\nDesign prospective evaluations that measure whether model-ranked compounds outperform conventional selection approaches\n\nQuantify uncertainty, identify out-of-domain predictions, and propose experiments that distinguish competing chemical hypotheses\n\nPartner with formulation chemists and machine-learning researchers to recommend the next compound, dose, formulation, or controlled variant to test\n\nBuild reproducible computational workflows with traceable structures, descriptors, model versions, and experimental outcomes\nQualifications\n\nPh.D. or equivalent research experience in computational chemistry, chemoinformatics, physical chemistry, medicinal chemistry, chemical engineering, or a related field\n\nExperience with molecular descriptors, similarity methods, QSAR, molecular machine learning, or graph-based models\n\nStrong Python skills and experience with tools such as RDKit or equivalent chemical-computing libraries\n\nAbility to design leakage-resistant evaluations and interpret model performance in chemical rather than purely statistical terms\n\nClear scientific writing and close collaboration with experimental teams\nDesired Attributes\n\nExperience with active learning, Bayesian optimization, uncertainty calibration, or prospective molecular discovery\n\nKnowledge of volatility, solubility, controlled release, odorants, or insect-active small molecules\n\nExperience connecting computation to iterative wet-lab experiments\n\nInterest in building open, reusable scientific methods rather than a one-time screening model\nTo learn more, visitmonarchlabs.org","description_format":"text","description_chars":4427,"description_truncated":false,"requirements":{"experience_years_min":null,"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":"2026-10-12T20:36:31Z"}],"visa":[],"liveness":{"score":3,"band":"cold","label":"Long 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