About the Exploratory Sciences Team

You will be joining the expanding “Exploratory Sciences” team, focused on tackling proteins which are critical for disease biology, but have been traditionally considered intractable.

We are a multidisciplinary team within the Drug Design and Medical Research function at Isomorphic Labs. We work where machine learning, chemical biology and drug discovery meet, applying the Isomorphic Labs platform to some of the hardest problems in the field.

Our work goes beyond individual targets. We explore emerging therapeutic modalities, molecular glues and peptides to modalities that don't have a playbook yet. We also design the data generation strategies that make these modalities workable for AI-driven design. The experiments we run don't just move drug-discovery programmes forward - they shape the models that power Iso's drug design engine.

We're as comfortable in silico as we are at the bench. Our scientists build and test hypotheses computationally, use LLM-based agentic workflows in their daily science, and work alongside ML researchers and engineers on Platform builds. We work closely with teams across Isomorphic Labs, so ideas move quickly from model to real world experiments and back again, keeping the goal of drug discovery and development front and centre.

Above all, we're curious and ambitious, and we care about growth. We put continuous learning and development first, because we're building the next generation of drug discovery scientists. If you want to help define what drug discovery looks like next, you'll fit right in.

Your impact

We're looking for a talented, hands-on in vitro pharmacologist to join Exploratory Sciences and help turn bold ideas about new therapeutic modalities into robust experimental data.

You'll be the experimental engine behind our exploratory programmes. You'll design, build and run the biochemical, biophysical and cellular assays that tell us whether a new modality works. You'll also help define what good data looks like when there's no established playbook.

You'll make the data count twice. Every experiment you design should move a programme forward and also make our models smarter. Working closely with ML and computational colleagues, you'll help find the places where the platform has blind spots and come up with new assays that fill them.

You'll be part of a new way of doing biology. You'll use LLM agentic workflows and in silico hypothesis building in your everyday science, and help shape the tools you and your colleagues depend on.

Ready to take the next step in your scientific career? Isomorphic Labs gives ambitious scientists a unique place to take on stimulating challenges, push their boundaries and accelerate their personal and professional growth.

What you will do

Assay Development & Experimental Execution:

• Design, develop and run biochemical, biophysical (e.g. SPR/GCI, nanoDSF, Spectral Shift, MST) and cellular assays for novel modalities.
• Generate quantitative data on binding kinetics, ternary complex formation, degradation and functional pathway readouts (e.g. HiBiT, NanoBRET, TR-FRET, AlphaLisa).
• Develop modality specific cellular assays that show target engagement and functional pathway effects, linking molecular data to therapeutic hypotheses.
• Profile novel chemical matter from our in silico design pipelines and interpret results to guide next design cycles.
• Help build and refine screening cascades for internal and partnered programmes.

Data Generation & Quality:

• Generate high-quality, reproducible, ML-ready datasets, applying agreed data quality, ontology and QC standards.
• Propose and test new assays or experimental formats that close gaps in our models.

AI, Agentic Workflows & Platform Builds:

• Use LLM agentic workflows and in silico tools to speed up literature review, hypothesis generation, experimental design and analysis.
• Work with cross-disciplinary colleagues to test in silico hypotheses in the wet lab and feed the results back into the platform.
• Contribute biological insight and user feedback to Platform builds.

Collaboration & External Partnerships:

• Work closely with medicinal chemistry, DMPK, computational chemistry and computational biology to drive SAR and compound progression.
• Design, coordinate and monitor wet-lab studies across the CRO ecosystem, making sure data is delivered on time and to standard.
• Present results clearly to project teams and stakeholders and help shape project decisions.
• Represent the company externally, interacting with existing and prospective collaborators.

Skills and qualifications

Essential:

• BSc, MSc or PhD in a relevant field of biology (Pharmacology, Biochemistry, Biophysics, Cell Biology or a related discipline)
• Extensive experience in the design and implementation of biological screening cascades applied to drug design projects from hit identification to candidate selection phases with relevant post-doctoral or industry drug discovery experience
• Strong hands-on expertise in in vitro pharmacology and/or biophysics, including assay development, optimisation and data analysis.
• Experience with, or a strong interest in, novel modalities (e.g. TPD, molecular glues, peptides, induced proximity).
• Experience with, or clear enthusiasm for, LLM agentic workflows, in silico hypothesis building and data-driven discovery.
• Highly collaborative, adaptable, and motivated by a fast-moving, interdisciplinary environment, with the ability to work across several projects and therapeutic areas.
• Communicates clearly with colleagues from very different scientific and technical backgrounds.

Nice to have:

• Experience working with CROs or external collaborators.
• Experience generating data to validate virtual screens, de novo designs or ML-predicted binders.
• Worked with Data Science groups in setting up systems for ingestion of biological data.
• General knowledge of machine learning modelling applied to biological systems and drug discovery
• Experience with lab automation and high-throughput data workflows.
• Scripting or data analysis skills (e.g. Python, R) or experience building agents or prompts for scientific workflows