Senior Biochemist (gn) - Enzymology & High-Throughput Screening @ Biotech Venture, Cambridge

About DropCode

DropCode is building the data engine for protein function. Starting with enzymes, we use our patented droplet microfluidics platform to capture exponentially more data on protein function than conventional methods, linking genotype to phenotype at per-droplet resolution, making every droplet a micro test tube. This data fuels machine learning models that learn in ever greater detail how sequence determines function. Our wedge is enzyme engineering for biocatalysis and industrial biotechnology, but our ambition is to make DropCode the definitive platform for protein function prediction.

We are Cambridge PhDs with deep expertise across microfluidics, biochemistry, machine learning, optics, and engineering. We believe the language of biology is machine learning, and that the fastest path to transformative models is not just better AI, it is better inputs.

The Role

We are looking for a senior biochemist with deep expertise in enzymology and high-throughput screening to own the biological layer of our platform. You will design and develop quantitative enzyme assays compatible with our droplet microfluidics workflow, build and characterise variant libraries, and be the bridge between rigorous biochemistry and ML-ready datasets.

You will work at the intersection of enzymology, assay engineering, and data science, ensuring that every experiment we run generates the richest, most quantitative signal possible for our models. This is not a role for someone who optimises a single assay to perfection; it is a role for someone who thinks in libraries, throughput, and information content.

What You'll Do

• Design and implement quantitative enzyme point readouts and kinetic assays (kcat, KM, specificity, stability) adapted for high-throughput screening in droplet microfluidics.

• Create standardised workflows for onboarding different enzymes and their assays.

• Build and characterise gene variant libraries for deep mutational scanning and directed evolution campaigns, including DNA barcoding and library construction.

• Iterate on DropCode’s barcoding platform to increase synthesis efficiency.

• Run droplet microfluidics workflows including droplet encapsulation, merging, picoinjection and imaging.

• Develop single-droplet and single-enzyme assay formats that maximise genotype–phenotype linkage fidelity and data quality.

• Lead high-throughput biochemical screening campaigns, ensuring outputs are calibrated, reproducible, and structured for ML ingestion.

• Collaborate closely with the ML team to design experiments that maximise information content and fill gaps in the model's understanding of the fitness landscape.

• Troubleshoot and iterate on assay formats rapidly - treating each run as a data generation event, not an optimisation exercise.

• Contribute to the development of DropCode's biocatalyst discovery capabilities and overall barcoding platform.

What We're Looking For

• Deep expertise in enzyme kinetics and mechanism — you understand what quantitative structure–function relationships really mean in practice.

• Hands-on experience designing and running high-throughput biochemical screens, ideally at a scale where data pipeline design matters.

• Experience with DNA barcoding, library construction, and genotype–phenotype linkage strategies (e.g., deep mutational scanning workflows).

• Familiarity with droplet microfluidics, compartmentalised assays, or single-cell / single-enzyme screening is a strong advantage.

• Quantitative mindset: comfortable with statistics, data interpretation, and thinking about experiments as structured data collection exercises.

• Ability to work fluidly across wet lab, microfluidics, and ML-integrated workflows without siloing into any single domain.

• PhD in biochemistry, chemical biology, enzymology, or a closely related field.

Who You Are

You are impatient with slow, artisanal biology and excited by the prospect of generating datasets at a scale that makes genuinely predictive models possible. You think about assay design in terms of information content, not just signal-to-noise. You are collaborative, move fast, and believe that the highest-value thing a biochemist can do right now is build the platform that teaches the next generation of protein models.