Location: Suzhou, China
We are seeking an exceptional Principal Scientist, Antibody Engineering to join our Biologics Research team in Suzhou. This is a hands-on bench scientist role for a deep technical expert who thrives at the interface of classical antibody engineering and cutting-edge computational approaches. You will lead and execute the engineering of therapeutic antibody candidates — from liability removal and humanization through affinity maturation and developability optimization — while actively applying AI/ML-based tools to accelerate and enhance every stage of the antibody optimization cycle.
This position is not primarily managerial. We are looking for a scientist who is equally comfortable overseeing protein science, interpreting developability findings, and deploying a structure-based generative AI model to design next-generation antibody variants. You will be a cornerstone of our antibody engineering capability, driving scientific excellence and platform innovation in a collaborative, fast-paced R&D environment.
Educational Background & Experience
– Ph.D. in Biochemistry, Protein Engineering, Immunology, Biophysics, Computational Biology, or a closely related discipline is strongly preferred.
– 5+ years of hands-on experience in antibody engineering within an industrial or advanced academic research setting.
– Demonstrated track record of successfully advancing antibody candidates through engineering optimization cycles, with evidence of impact (publications, patents, program milestones).
Technical Expertise — Wet Lab (Hands-On)
– Proven hands-on experience in liability engineering: identification, prioritization, and experimental validation of deamidation, oxidation, aggregation, and isomerization hotspot removal.
– Demonstrated expertise in antibody humanization: CDR grafting, framework selection, back-mutation analysis, and experimental validation of humanized variants is a plus.
– Strong direct experience in biophysical characterization and developability profiling: DSF/nanoDSF, DLS, SEC, AC-SINS, HIC, viscosity measurement, and solubility/stability assays.
– Familiarity with antibody expression systems (mammalian transient expression, yeast surface display), purification (Protein A/G, SEC), and standard molecular biology techniques.
AI/ML Competency — *Mandatory, Not Optional*
– Hands-on experience using one or more of the following (or equivalent tools): ProteinMPNN, RFdiffusion, ESMFold, AlphaFold2/3, Rosetta/PyRosetta, AbMPNN, or similar structure-based design platforms.
– Practical experience applying ML models for antibody property prediction (e.g., aggregation, stability, affinity, immunogenicity scoring).
– Ability to independently run computational workflows, interpret model outputs, and translate predictions into experimental hypotheses.
– Familiarity with generative AI approaches for sequence and structure design; experience with fine-tuning or adapting pre-trained models on antibody-specific datasets is highly desirable.
– Working knowledge of Python or equivalent scripting for data analysis and interfacing with computational tools is a strong advantage.
Collaboration & Communication Skills
– Strong ability to work effectively in cross-functional, matrixed environments.
– Excellent written and verbal communication skills in English; Mandarin proficiency is an advantage in the Suzhou context.
– Demonstrated ability to translate complex scientific data into clear, actionable insights for diverse audiences.
Tagged as: Life Sciences
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