{"id":2388629,"url":"https://alion.io/job/monarchlabs-ml-researcher","title":"ML Researcher","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":"2026-10-17T05:46:00Z"}},"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":151000,"max_usd":331000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-09-04T23:59:56Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-10-18T09:28:22Z","board_verified":true,"closed_at":null,"days_open":43,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":43},"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.\nDevelop the learning methods that turn repeated assays into better scientific decisions. The central question is prospective: can a model use prior compound, assay, and behavior data to recommend an experiment that is more informative than the one scientists would otherwise run?\nKey Responsibilities\nResearch models that combine molecular information, formulation and dose, assay metadata, video-derived behavior, and laboratory context\nDevelop active-learning and sequential experiment-selection methods that balance predicted efficacy, uncertainty, novelty, and information value\nDefine retrospective and prospective evaluations, including holdouts by chemical scaffold, laboratory, colony, and time\nInvestigate which behavioral signals generalize across experiments and which reflect confounding, measurement noise, or laboratory-specific effects\nTranslate model failures into new labels, assay variants, controls, or experiments that improve the next training cycle\nCommunicate results with enough precision that experimental scientists can understand why a recommendation should or should not be trusted\nQualifications\nPh.D. or equivalent research record in machine learning, statistics, computational science, or a closely related field\nDemonstrated ability to formulate open-ended research questions, build strong baselines, and design evaluations that survive distribution shift\nStrong software skills in Python and a modern machine-learning framework\nExperience working with noisy, limited, multimodal, or experimentally generated datasets\nAbility to move between theory, implementation, and scientific interpretation\nDesired Attributes\nExperience with active learning, Bayesian optimization, reinforcement learning, causal inference, or scientific foundation models\nExperience in molecular discovery, biology, animal behavior, robotics, or another domain where models learn from physical experiments\nTrack record of prospective validation rather than benchmark-only research\nStrong research taste and comfort abandoning an attractive idea when the evidence does not support it\nTo learn more, visit monarchlabs.org","description_format":"text","description_chars":2338,"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":22,"band":"cold","label":"Long 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