Sr or Pri Scientist, Protein Engineering

Responsibilities

1.Design novel or engineered protein variants (antibodies, enzymes, binders, scaffolds, or other biologics) using structure-based design, molecular dynamics, and generative deep learning (e.g., RFdiffusion, ProteinMPNN, ESM, AlphaProteo, or internally developed architectures);

2.Design and optimize protein antigens and therapeutic proteins for mRNA-encoded expression, including sequence-humanization, stability engineering, and developability assessment to ensure efficient in vivo translation and expression from LNP-delivered mRNA;

3.Build and apply predictive models for protein stability, binding affinity, specificity, immunogenicity, developability, and expression;

4.Design and prioritize variant libraries for high-throughput screening and directed evolution campaigns (including language-model-guided evolution, e.g., EVOLVEpro);

5.Benchmark and integrate new structure-prediction and design models as the field evolves (e.g., AlphaFold3, Chai, Boltz-2);

6.Collaborate closely with platform teams to translate computational predictions into experimental designs;

7.Communicate findings, model performance, and design rationale clearly to both computational and experimental stakeholders, including cross-functional and leadership audiences;

8.(Principal level) Set technical strategy for computational protein design efforts, mentor junior scientists, and represent the computational function in cross-functional program planning.

Requirements

1.PhD in Computational Biology, Biophysics, Bioengineering, Computer Science, Computational Chemistry, or a related field.

2.5+ years (Senior) or 8+ years (Principal) of hands-on experience in computational protein design/engineering in an academic or industry setting.

3.Deep familiarity with protein structure prediction and design tools (e.g., Rosetta, AlphaFold/AlphaFold-Multimer, RFdiffusion, ProteinMPNN, ESM, Chai, Boltz) and molecular dynamics simulation (e.g., GROMACS, OpenMM, Desmond).

4.Strong programming skills; experience building and maintaining computational pipelines.

5.Experience partnering with wet-lab teams to design experiments and interpret biophysical/functional data.

6.Track record of taking computational designs from hypothesis through experimental validation, ideally within a therapeutic or industrial biologics context.

7.Excellent written and verbal communication skills; ability to explain computational concepts to non-computational colleagues.

8.Publication record and/or patents in protein engineering, structural biology, or computational biology is a plus.