{"id":3764890,"url":"https://alion.io/job/lila-sciences-co-op-bayesian-methods-for-complex-decision-making","title":"Co-Op, Bayesian Methods for Complex Decision Making","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":"A","score":88,"open_postings":39,"ghost_share":0,"stale_share":0.282,"repost_share":0,"time_to_fill_p50_days":85,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Early Careers","role_family":"Early Careers","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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"In-Context Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Multimodal AI","optional":true}],"status":"live","first_seen_at":"2026-10-30T12:36:02Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-08T00:49:01Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"Your Impact at LILA\nLila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, we work with experimental teams to build autonomous discovery agents that decide which experiments Lila's AI Science Factories should run next.\nReal scientific campaigns rarely look like textbook optimization problems. A single campaign can mix large discrete choices (which components, which recipe) with continuous settings (amounts, temperatures, times), pursue several objectives under hard constraints, and draw on evidence from literature and characterization data. Every experiment costs real time and materials. Off-the-shelf methods often struggle here, and they ignore the domain structure that could guide the search.\nAs a Bayesian Methods Co-Op, you will work alongside research scientists and engineers on a focused project within one broad question: how AI systems can make better experimental decisions. Depending on your background, that could mean building agents that choose the next experiment, testing when in-context learners can be trusted as decision-makers, or developing Bayesian strategies for tight experimental budgets. Your methods will be tested against live campaigns.\nWhat You'll Be Building\nDesign and benchmark experiment-selection strategies that balance exploration and exploitation under realistic budgets\nBuild autonomous discovery agents that propose the next experiment from surrogate models\nDevelop in-context learning approaches inspired by Bayesian inference, evaluating calibration and robustness\nRun retrospective and live evaluations on active campaign data with clear baselines and metrics\nPresent findings to ML and experimental teams, with the option to contribute to a publication\nWhat You'll Need to Succeed\nPursuing a Master's or PhD in statistics, ML, CS, operations research, applied math, or a quantitative science\nSolid grounding in Bayesian inference and decision making, including priors and posterior predictive distributions\nResearch experience in Gaussian processes, Bayesian optimization, bandits, reinforcement learning, or probabilistic modeling\nStrong Python skills with PyTorch and probabilistic libraries such as Pyro, PyMC, or BoTorch\nComfort working with small, noisy, and heterogeneous datasets\nClear communication with both ML and experimental collaborators\nBonus Points For\nApplying these methods to real problems in materials science, chemistry, or another physical science\nFamiliarity with in-context learning as implicit Bayesian inference, meta-learning, or prior-data fitted networks\nOptimization over categorical or combinatorial spaces, including mixed-variable, multi-objective, or batch settings\nReinforcement learning for sequential decision making or Bayesian experimental design\nTraining models on multimodal scientific data such as text, images, spectra, or simulations\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. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.","description_format":"text","description_chars":4658,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Materials Science & Nanotechnology","Laboratory & Pharmaceutical Robotics","AI Research Labs","AI for Science"],"lifecycle":[{"event":"open","at":"2026-11-04T19:42:09Z"}],"visa":[{"country":"US","licensed_sponsor":true,"adverts_offering_sponsorship_24m":0,"sanctioned_now":false,"evidence":"H-1B filings in 12 months: 29","filings_12m":29,"filings_prev_12m":4,"green_card_filings_12m":0,"median_offered_wage_usd":157500,"route":null,"cap_exempt":false,"checked_at":"2026-11-07T07:57:00+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":7,"expected_fill_days":85,"reasons":["conf:1","velocity","win:early","comp:brand"],"computed_at":"2026-11-07T05:47:08Z"},"pay":null,"html_url":"https://alion.io/job/lila-sciences-co-op-bayesian-methods-for-complex-decision-making","json_url":"https://alion.io/job/lila-sciences-co-op-bayesian-methods-for-complex-decision-making.json","meta":{"generated_at":"2026-11-08T01:39:24Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":3650,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-09T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":65163},"rest":"https://alion.io/mcp/rest/get_company?id=65163"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Flila-sciences-co-op-bayesian-methods-for-complex-decision-making"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Flila-sciences-co-op-bayesian-methods-for-complex-decision-making"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Flila-sciences-co-op-bayesian-methods-for-complex-decision-making"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/lila-sciences-co-op-bayesian-methods-for-complex-decision-making\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Flila-sciences-co-op-bayesian-methods-for-complex-decision-making"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3764890}},{"tool":"search_jobs","arguments":{"company":"lila.ai","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3764890},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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