{"id":3300741,"url":"https://alion.io/job/lila-sciences-co-op-reaction-prediction-benchmarking","title":"Co-Op, Reaction Prediction & Benchmarking","company":{"id":65163,"name":"Lila Sciences","domain":"lila.ai","url":"https://alion.io/company/lila-ai","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":84,"open_postings":39,"ghost_share":0,"stale_share":0.436,"repost_share":0,"time_to_fill_p50_days":78,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Biotech","role_family":"Biotech","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cambridge, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":143000,"max_usd":327000,"period":"year","method":"role_country_seniority_unknown","sample_n":1243},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pre-training","optional":true},{"name":"Python","optional":true},{"name":"Self-Supervised Learning","optional":true}],"status":"live","first_seen_at":"2026-10-26T20:51:50Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-11-12T10:28:13Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"Your Impact at LILA\nDevelop and evaluate machine learning methods supporting small molecule design and synthesis. Working with public and internal chemistry datasets, the co-op will pursue a focused research project in either reaction prediction and benchmarking or molecular property prediction for medicinal chemistry optimization.\nPotential projects include building benchmarks to assess reaction prediction models and reaction plausibility, or developing and fine-tuning molecular property predictors using project-specific data. The primary project will be selected based on the candidate’s interests, experience, and team priorities. Expected outputs include reproducible data and evaluation workflows, benchmark results, and research recommendations to inform future model development and integration.\nWhat You'll Be Building\nPlan and execute a Co-Op project in conjunction with the Molecular Intelligence team\nWhat You'll Need to Succeed\nPursuing a graduate degree (MSc, PhD) in Chemistry, Computer Science, Physics, or similar\nBonus Points For\nPython (data processing, model training, and evaluation)\nFamiliarity with cheminformatics libraries (RDKit or similar)\nBasic machine learning (supervised learning, model evaluation metrics)\nFamiliarity with chemistry concepts through coursework or research experience\nBonus: experience with molecular property prediction, multi-task learning, self-supervised pre-training, or model fine-tuning\nBonus: familiarity with reaction data formats, reaction SMARTS, or reaction databases (e.g., Pistachio, Reaxys, SciFinder)\nBonus: background or interest in organic or medicinal chemistry\nAbout LILA\nLila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\nLILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\nGuided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.\nWe’re All In\nLila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.\nInformation you provide during your application process will be handled in accordance with our Candidate Privacy Policy.\nA Note to Agencies\nLila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. 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