As a Full Stack Software Engineer, you will design and develop web applications for drug discovery, collaborating with scientists and product teams, and creating cloud-native applications.

Manage CRO relationships for drug discovery projects, implement CRO management systems, monitor project efficiency, and drive contract negotiations with stakeholders.

Lead and build a high-performance engineering team focused on creating a machine learning infrastructure platform; collaborate with diverse teams to ensure operational excellence and scalability of models.

The DMPK Lead will define PBPK strategies to support drug design, oversee development DMPK strategies, and ensure regulatory compliance in drug candidate delivery.

Join Isomorphic Labs as a Senior Research Scientist in In-vitro/Cellular Pharmacology, focusing on peptide-based drug discovery and collaborating with cross-functional teams to drive projects toward clinical application.

As a Cloud Platform Engineer, you will design and manage cloud infrastructure while collaborating with diverse teams to enhance software development practices and contribute to technical decisions.

You will contribute to the inference platform, maintain APIs, support users, improve CI/CD, and ensure platform reliability.

Isomorphic Labs seeks to recruit interested candidates for roles in software engineering, machine learning research, computational chemistry, and project management for future openings.

As a Software Engineer, you will design and manage cloud infrastructure, enhance DevOps practices, and collaborate with teams to drive innovation in drug discovery.

Design and develop novel machine learning models and training algorithms for drug discovery. Lead and run research projects, implement and scale training/inference frameworks, analyze experiments, and collaborate across disciplines. Mentor and provide technical guidance to other ML researchers and contribute to research direction and presentations.

Lead DMPK strategy for drug discovery projects, design data-driven ADME/PK experiments and screening cascades, integrate DMPK/PD/tox data for decision-making, define quantitative pharmacology approaches, author regulatory DMPK sections, and collaborate with chemistry, preclinical, clinical, and ML teams to optimize molecule design and predictive models.

Lead the development of quantitative pharmacology strategies for drug discovery projects, leveraging AI models for biologics and small molecules, while mentoring junior team members.

As a Research Scientist focused on Large Language Models, you'll develop and apply advanced ML techniques in drug discovery, contributing to groundbreaking research and leading projects in a collaborative environment.

Lead and operationalize engineering roadmaps, translating technical strategy into delivery plans. Own key engineering workstreams, remove blockers, manage risks and technical debt, define engineering metrics, and align engineering output to product and organizational objectives.

Own and drive multiple research roadmaps in AI-driven drug discovery: define milestones, coordinate Research Scientists and ML Engineers, track experimental progress, surface bottlenecks, set technical sync rhythms, and ensure cross-team alignment to move projects from concept to technical milestones.

Lead a team to design and develop scalable machine learning systems for drug discovery, mentoring engineers and ensuring software excellence.