{"id":1292955,"url":"https://alion.io/job/axiom-bio-ml-researcher","title":"ML Researcher","company":{"id":2041615,"name":"Axiom Bio","domain":"axi.om","url":"https://alion.io/company/axi-om","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":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-02T06:13:15Z"}},"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":["South San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":153000,"max_usd":339000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Computer Vision","optional":false},{"name":"Contrastive Learning","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Interpretability","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mass Spectrometry","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Polars","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"TensorFlow","optional":false}],"status":"closed","first_seen_at":"2026-07-11T03:44:55Z","employer_posted_date":"2026-07-11","last_verified_at":"2026-11-01T05:52:55Z","board_verified":false,"closed_at":"2026-11-01T05:52:55Z","days_open":113,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":112},"description":"About Axiom\nAxiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.\nWhat you will do\nYou will help define Axiom’s core ML research agenda and build the models that power our product.\nDefine end-to-end ML and agent systems spanning wet-lab data generation, data cleaning, feature extraction, representation learning, model training, evaluation, inference, deployment, and customer-facing outputs.\n\nBuild novel models that learn the relationship between chemistry, biological response, dose, exposure, and human toxicity.\n\nTrain large multimodal models on paired chemical structures, high-content cellular images, transcriptomics, proteomics, mass spectrometry, ADME, and clinical outcome data.\n\nDevelop foundation models and representation-learning systems for biological images, molecules, and multimodal experimental readouts.\n\nArchitect models that predict human toxicity as a function of dose, Cmax, in vitro potency, chemical structure, and biological state.\n\nDevelop new ways to aggregate, pool, align, and interpret embeddings across assays, doses, timepoints, modalities, compounds, and biological systems.\n\nWork on contrastive learning, self-supervised learning, semi-supervised learning, multimodal learning, graph neural networks, biological image models, generative models, and mechanistic reasoning systems.\n\nBuild models that can generalize across chemical space, mechanisms, targets, assays, and customer programs.\n\nConduct rigorous error analysis to understand when models fail, why they fail, and what data would make them better.\n\nCollaborate with computational biologists, chemists, mass spec scientists, data engineers, and wet-lab teams to design experiments that maximally improve model performance.\n\nHelp build Axiom’s mechanistic agents: systems that reason over experimental data, compare compounds to mechanistic neighbors, explain toxicity mechanisms, and guide scientific decisions.\n\nOwn the research-to-product loop: prototype, train, evaluate, ship, observe real usage, improve, and repeat.\n\nShip insanely great models and products to customers.\n\nResearch areas we are excited about\nWe are especially interested in people excited by:\nMultimodal ML across chemistry, cellular imaging, transcriptomics, proteomics, mass spectrometry, ADME, and clinical outcomes.\n\nReasoning over massive amounts of multimodal experimental data, model outputs, literature, and mechanistic evidence.\n\nReinforcement learning for basic biology, chemistry, and advanced drug discovery.\n\nSelf-supervised and semi-supervised learning on high-content imaging and biological readouts.\n\nUncertainty estimation, calibration, and confidence for scientific decision-making.\n\nMechanistic interpretability for biological and chemical models.\n\nEvaluation systems for models that must perform on real drug discovery problems, not toy benchmarks.\n\nWhat we are looking for\nWe are looking for someone with exceptional ML talent, strong engineering ability, and the ambition to become a leader in AI for biology and drug discovery.\nYou might be a great fit if:\nYou have done at least one piece of work, in industry, academia, open source, or independently, that shows exceptional machine learning ability.\n\nYou are deeply technical and comfortable writing PyTorch, debugging training runs, working with messy data, scaling inference, and building real systems.\n\nYou are excited by non-standard, thorny modeling problems where the data is noisy, multimodal, sparse, biased, biological, and deeply important.\n\nYou want to work on ML problems where better models can directly change scientific and clinical decisions.\n\nYou are not afraid of the data dirty work required to make models better.\n\nYou can move between research ideas and production systems.\n\nYou care about evaluation, calibration, failure modes, and real-world usefulness.\n\nYou are curious enough to learn biology, chemistry, toxicology, pharmacology, and drug discovery.\n\nYou want to grow as both a researcher and an entrepreneur.\n\nYou want your work to become a product that customers love and rely on.\n\nTechnical skills we value\nWe do not expect every candidate to have all of these, but we are especially excited by experience with:\nPyTorch, JAX, TensorFlow, or other deep learning frameworks.\n\nPython, NumPy, Pandas, Polars, PyArrow, scikit-learn, and scientific computing.\n\nTraining and evaluating deep neural networks at scale.\n\nRepresentation learning, embeddings, contrastive learning, metric learning, and self-supervised learning.\n\nComputer vision models, especially for biological imaging, microscopy, cell painting, or high-content screening.\n\nMultimodal ML across images, molecules, text, omics, mass spec, or tabular data.\n\nLarge-scale model training, distributed training, GPU infrastructure, inference pipelines, and cloud compute.\n\nModel evaluation, ablations, benchmarking, uncertainty estimation, calibration, and interpretability.\n\nLLMs, agents, retrieval, tool use, and reasoning systems.\n\nBiology, chemistry, toxicology, pharmacology, or drug discovery datasets.","description_format":"text","description_chars":6080,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Drug Discovery","Drug Discovery AI"],"lifecycle":[{"event":"open","at":"2026-09-26T08:32:57Z"},{"event":"close","at":"2026-11-01T05:52:55Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"Filed H-1B visas in the last two years","filings_12m":0,"filings_prev_12m":1,"green_card_filings_12m":0,"median_offered_wage_usd":null,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":null,"pay":null,"html_url":"https://alion.io/job/axiom-bio-ml-researcher","json_url":"https://alion.io/job/axiom-bio-ml-researcher.json","meta":{"generated_at":"2026-11-02T17:32:45Z","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":24455,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-03T00:00:00Z"}},"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":2041615},"rest":"https://alion.io/mcp/rest/get_company?id=2041615"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Faxiom-bio-ml-researcher"},{"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%2Faxiom-bio-ml-researcher"},{"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%2Faxiom-bio-ml-researcher"},{"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/axiom-bio-ml-researcher\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Faxiom-bio-ml-researcher"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":1292955}},{"tool":"search_jobs","arguments":{"company":"axi.om","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":1292955},"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. Docs: https://alion.io/developers"}}