Jobs at Isomorphic Labs

Own the end-to-end candidate experience, including complex global interview scheduling, candidate communications, onsite or virtual interview hosting, ATS data integrity, onboarding compliance, feedback analysis, and process improvement. The role partners with hiring managers and talent leaders, documents and scales recruiting workflows, supports training, and uses data and AI to improve talent acquisition operations.

Oversee Isomorphic Labs’ Technical Operations team, delivering user-facing IT support, application integration, and platform operations. Manage application lifecycles, improve technical support processes across the Alphabet and Google ecosystem, automate operational workflows, and shape systems administration strategy. Lead, mentor, and grow a high-performing team while partnering with Workplace, Engineering, scientists, and other stakeholders on the technical operations roadmap.

Build and evolve large-scale machine learning infrastructure for training and inference workloads. Develop reliable, performance-critical platforms, explore agent-based platform improvements, and collaborate with engineering, research, and product teams. The role requires strong Python skills and experience building scalable infrastructure or platforms, with Kubernetes, GCP, multi-cloud, and ML infrastructure expertise preferred.

The Principal Software Engineer will ensure the reliability and scalability of AI systems, leading infrastructure improvements and optimizing ML workflows.

Research Engineer responsible for optimizing large language model post-training and scaling frontier models for drug discovery. The role involves distributed training and inference performance optimization, fine-tuning and reinforcement learning framework evaluation, bottleneck diagnosis, production system deployment, and low-precision methods. The engineer will collaborate with research and engineering teams across machine learning, computational biology, and chemistry.

Lead DMPK strategy for drug discovery programs using AI-first approaches. Design screening cascades and mechanistic experiments, integrate DMPK, pharmacodynamic, and toxicology data, support dose and safety-window predictions, define quantitative pharmacology strategies, optimize molecule design, improve human pharmacokinetic predictions, and author regulatory documents. The role requires close collaboration with chemistry, preclinical, clinical, pharmacology, and technology teams.

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.

As a Research Engineer, you will develop and optimize AI models for drug discovery, collaborating with scientists to advance ML applications in biopharmaceuticals.

Leads multi-quarter information security roadmaps and complex security initiatives across cloud security, IAM, Secure SDLC, regulated data transfers, and agentic AI workflows. Partners with engineering, machine learning, research, and business teams to deliver security programs, manage risk and dependencies, advocate for InfoSec capabilities, and provide CISO-level visibility through automated dashboards and operational metrics.

Lead and scale the nonclinical safety function for early development programs, define and execute phase-appropriate toxicology strategies, integrate AI/in silico predictions with traditional data, oversee post-IND GLP studies (including chronic, reproductive, carcinogenicity), establish translational biomarker and exposure-based modeling, manage CRO relationships, and lead regulatory strategy and submissions (IND, IB, DSUR, NDA/MAA) including presenting AI-derived safety data to health authorities.

Operate and secure self-hosted multi-user vendor applications: deploy and maintain Kubernetes-based instances, build CI/CD pipelines and IaC, integrate SSO/RBAC, run vulnerability scanning and remediation, automate rollouts across endpoints, and manage monitoring, backups, and low-downtime upgrades.

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.

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

Lead bioanalytical strategy across small molecules, biologics, and ADCs from discovery through preclinical GLP/non-GLP studies. Design, validate, transfer PK/PD and ADA assays, manage global CROs and complex cold-chain logistics, integrate bioanalytical data with AI models, author IND/IB bioanalytical sections, and represent bioanalysis in cross-functional teams to advance candidates to clinic.

Lead design and execution of in vitro and cellular pharmacology strategies for drug discovery projects. Develop and optimize biophysical, biochemical, and cellular assays (SPR, ITC, enzymatic, phenotypic), define screening cascades, collaborate with AI/ML, medicinal chemistry, and structural biology, manage CRO studies, interpret quantitative binding and dose-response data, and support candidate selection toward IND-enabling packages.