The posting
Description:
Lead Axiom’s drug discovery programs by combining medicinal chemistry, human biology, and scientific AI agents to develop safer, more effective drugs. Own scientific decisions from molecular design through experimental validation, working with pharmaceutical partners to advance candidates toward development and turn each program’s discoveries into better agents and tools.
About Axiom:
Axiom’s mission is to eliminate unexpected drug toxicity and unlock an abundance of ultra safe medicines. We are starting with the liver, where toxicity remains a leading cause of drug failure. Our high-throughput human liver safety platform combines advanced human cell systems with imaging, transcriptomics, mass spectrometry, and drug metabolism measurements. Our AI agents integrate these data with chemical structure, human exposure, and thousands of clinical reference compounds to predict whether a drug will be toxic in humans, why, and how to make it safer.
We work with seven of the world’s top twenty pharmaceutical companies to design safety into molecules from the earliest stages. This applied work feeds directly into our partnerships with leading frontier AI labs, turning real discovery challenges and experimental feedback loops into scientific skill trees and reinforcement learning environments that train increasingly superhuman scientists. We then deploy these frontier models to make cancer therapies safer, unlock treatments for chronic diseases that demand exceptional safety, revive promising drugs abandoned because of toxicity, and much more.
Our ultimate ambition is to build a superhuman AI agent that predicts how a drug will behave across all major human organ systems before the first patient receives it. With insanely accurate safety predictions, we aim to enable rapid, high-throughput testing of novel drugs in humans with zero risk of toxicity. This is the key to unlocking an abundance of ultra safe, effective medicines.
What you will do:
- Lead Axiom drug discovery programs from initial hypothesis through lead optimization, integrating medicinal chemistry, human biology, computational methods, and AI agents to develop safer, more effective drugs.
- Help select programs where Axiom has a distinctive advantage, define target product and compound profiles, and establish clear milestones and go/no-go criteria.
- Own the design–make–test–analyze cycle, working with computational scientists, experimental teams, and synthesis partners to design molecules, prioritize experiments, interpret results, and decide what to do next.
- Use and help develop scientific agents that investigate toxicity mechanisms, integrate evidence, propose chemical modifications, and guide experimental decisions.
- Own scientific relationships and deliverables for biotech and pharmaceutical partners, translating their program goals into experimental plans, rigorous analyses, and actionable recommendations.
- Ensure the quality and reproducibility of program data, distinguish meaningful findings from artifacts, and test whether improvements in potency, safety, and exposure support the intended drug profile.
- Work with Axiom’s scientific and commercial leadership to build compelling evidence packages for partnerships and licensing opportunities before clinical development.
- Turn insights from internal programs and customer engagements into reusable tools, analyses, and agent workflows within Translate.
- Partner with ML researchers and leading AI labs to turn experimental campaigns into benchmarks and reinforcement learning environments that improve frontier models’ scientific judgment and molecular design.
What we look for:
- A track record of advancing drug discovery programs through hit-to-lead, lead optimization, or candidate nomination, with direct responsibility for consequential scientific decisions.
- Strong medicinal chemistry judgment, including interpreting structure–activity and structure–property relationships, designing informative compound series, and balancing competing objectives.
- Fluency in ADME/DMPK, PK/PD, and exposure–response relationships, with the ability to connect experimental measurements to the profile needed for a successful drug.
- The ability to integrate chemistry, biology, toxicology, and computational evidence into clear hypotheses, decisive experiments, and well-supported go/no-go recommendations.
- Experience building or using scientific AI agents, with the curiosity to rethink discovery workflows and the judgment to verify outputs against experimental evidence.
- End-to-end ownership: you can turn an ambitious program goal into an experimental and analytical plan, coordinate execution, and adapt quickly as results arrive.
- Strong scientific communication and the ability to earn the trust of biotech and pharmaceutical partners, understand their priorities, and translate findings into program decisions.
Technical skills we value:
- Medicinal chemistry, compound design, SAR analysis, matched molecular pairs, and multi-parameter optimization.
- ADME/DMPK, PK/PD, exposure–response analysis, human dose projection, and safety margin assessment.
- Mechanistic toxicology, drug metabolism, reactive metabolites, and interpretation of human cell-based assays.
- Scientific AI agents for literature and patent research, data analysis, hypothesis generation, molecular design, and experimental planning.
- Python, RDKit, NumPy, pandas, SQL, and reproducible analytical workflows.
- Experimental design, dose–response modeling, statistical analysis, assay validation, and integration of imaging, transcriptomic, and biochemical data.
- Clear scientific presentations, program reviews, and evidence packages that support partner decisions and licensing discussions.
