Physical & Life Scientist
Physical and life scientists in AI companies design and execute experiments across biology, chemistry, and physics to accelerate drug discovery and therapeutic development. These roles span from wet lab work—running immunological assays, synthetic chemistry, and analytical separations—to computational approaches like molecular dynamics simulations and pharmacometric modeling. What distinguishes these scientists is their direct integration with AI-driven platforms: they validate predictions from machine learning models, generate training data for foundation models in biology and chemistry, conduct safety evaluations of AI systems in scientific domains, and translate computational designs into experimental reality. They typically sit within discovery and development teams at TechBio and AI companies, collaborating closely with computational researchers, engineers, and medicinal chemists to bridge the gap between digital prediction and physical validation.
Skills
What companies are looking for in this role.
Designing and executing functional assays for biological characterization and validation
Developing and optimizing cell-based assays including immune activation and signaling studies
Performing hands-on molecular and cellular biology experiments including mammalian cell culture
Troubleshooting complex experimental workflows and resolving procedural or technical issues
Conducting immune profiling and functional characterization using multi-parameter flow cytometry
Designing, building, and validating protein display libraries for selection and screening
Executing high-throughput protein expression, purification, and characterization workflows
Performing molecular cloning and DNA assembly for library construction
Leading discovery programs through target validation and lead candidate development
Performing next-generation sequencing library preparation and data quality assessment
Designing and executing synthetic chemistry routes and chemical transformations
Developing drug-like compound properties through pharmacological and pharmacokinetic assessment
Managing inventory, reagent preparation, and sample tracking for high-throughput operations
Integrating experimental data generation with predictive computational modeling and machine learning
Designing automated and semi-automated assay workflows for high-throughput screening platforms
Collaborating with machine learning and computational biology teams to guide model training and evaluation
Building predictive models for drug absorption, distribution, metabolism, and excretion
Designing clinical pharmacology and pharmacometric strategies for drug development programs
Translating synthetic methodologies onto automated chemistry platforms
Cross-functional collaboration with protein engineering, automation, and computational biology teams
Communicating scientific findings and experimental data clearly across teams and stakeholders
Working independently and as part of multidisciplinary project teams
Maintaining detailed experimental records and ensuring reproducibility of workflows
Managing multiple projects simultaneously with shifting priorities in dynamic environments
Leading and developing high-performing research teams in fast-paced environments
Evaluating and deploying new scientific technologies and methodologies
Providing mechanistic insights and guiding therapeutic program strategy decisions
Interfacing with external partners and health authorities as a subject matter expert
Authoring regulatory documents including investigator brochures and regulatory submissions
Technology
The tools and technologies that define this role.
Open Jobs
21 open Physical & Life Scientist jobs across 7 companies.
Other Research & Science roles
Scientists conducting original research to advance the state of the art in AI, machine learning, and related fields.
Engineers who build the systems, tools, and infrastructure that enable research.
Senior individual contributors at AI labs working on core model development, pre-training, post-training, and model optimization.
Leaders who manage research teams, set research agendas, and guide scientific strategy.
Scientists who apply machine learning techniques to solve specific product or domain problems.