About the RoleYou will build machine-learning systems that remove real bottlenecks from drug discovery and development. The role spans research and engineering: identifying valuable problems, adapting modern biomolecular models, building tools for scientists, and closing the loop between model predictions and wet-lab results. Success is measured by whether the systems accelerate better experiments and better drug-development decisions.
Responsibilities- Work with scientists to identify high-value bottlenecks in drug discovery and development where machine learning can materially improve speed or decision quality.
- Build systems for experiment planning, literature triage, protocol drafting, in silico screening, candidate generation, filtering, and predictive analysis.
- Fine-tune and apply biomolecular models such as ESM, AlphaFold-family models, RFdiffusion, ProteinMPNN, and related approaches using Capable's data.
- Develop candidate-analysis workflows that may include molecular dynamics, post-training, probing, evaluation, and other fit-for-purpose computational methods.
- Work directly with wet-lab scientists and operators to automate preclinical or clinical-development workflows and make tools usable in practice.
- Build active-learning loops that connect in silico predictions to in vivo results.
- Create internal evaluations that measure whether models and tools improve experimental throughput, candidate quality, or program decisions.
Ideal Qualifications- Strong research judgment in biomolecular modeling and drug development, or a demonstrated ability and desire to develop that judgment quickly.
- The ability to own an ambiguous problem end to end, from identifying the useful question through building, evaluating, and improving a working system.
- A practical interest in wet-lab reality and in building tools around the constraints of experiments, operators, data quality, and scientific decisions.
- Curiosity, strong analytical instincts, and a habit of testing whether a method creates real-world value rather than relying on benchmark performance alone.
- Experience with active learning, data-constrained biological modeling, multimodal omics, imaging, phenotypic data, or production-scale agent platforms is helpful but not required.
Pay & Benefits- In addition to the posted salary range, we offer generous equity options.
- Compensation will depend on the skills, experience, and scope of impact you bring. If your background, experience, or compensation needs fall outside this range, we are still open to a conversation based on the scope and impact you could bring.
- $500+ monthly wellness budget for training, supplements, coaching, recovery, or other tools that help you perform at your best.
- Healthcare: We offer a broad range of medical, dental, and vision coverage options, with Capable covering 100% of the base policy.
- You will also have access to HSA, FSA, and 401K plans.
- Healthy dinners with the team are provided daily.
- We support visa sponsorship where appropriate, including O-1, H-1B, J-1, TN, and other employment-based pathways. Our team is already international, with members from Canada, Pakistan, Germany, Austria, Switzerland, China, India, and the United States.
Application ProcessOur process is fast, transparent, and personal. We care about getting to know the person behind the application.
- Initial application
- First phone screen
- Second phone screen
- In-person work trial in San Francisco: designed to give both sides a realistic sense of working together. Get to know the founding team!
Apply TodayCurious but not sure? Apply anyway. It takes 5 minutes, and you do not need to be actively job searching to start a conversation.
Some of the best people do not map perfectly to a job description. If this sounds like a place where you could do important work, we would like to hear from you.
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