Job Title: Principal Scientist I, Antibody Engineering

Location: Suzhou, China

Position Summary

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.

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Key Responsibilities

1. Hands-On Antibody Engineering & Optimization

Execute the full spectrum of antibody optimization with direct bench involvement, from early liability identification through final candidate selection.

Perform liability engineering to identify and resolve deamidation, oxidation, isomerization, and aggregation hotspots

Lead affinity maturation campaigns using phage display, yeast display, or other directed evolution platforms

Execute antibody humanization strategies including CDR grafting, framework selection, and back-mutation analysis

Drive developability optimization through biophysical profiling — addressing viscosity, solubility, thermal stability, and polyspecificity liabilities

2. AI-Aided Protein Optimization & External Partner Collaboration

Serve as the internal champion for AI-driven antibody optimization, working closely with external AI/ML CROs and technology vendors to integrate computational design into engineering workflows.

Coordinate with AI/ML CROs to design and execute computational optimization campaigns, defining project scopes, deliverables, and success criteria

Evaluate and select appropriate external AI/ML platforms for specific engineering challenges

Translate computational sequence proposals into structured wet-lab validation experiments, closing the loop between in silico design and in vitro outcomes

3. Cross-Functional Leadership & Program Delivery

Represent antibody engineering expertise within multidisciplinary project teams, providing scientific guidance that shapes program strategy and accelerates candidate progression.

Partner with discovery, structural biology, CMC, and clinical teams to align engineering strategies with broader program objectives

Drive developability risk assessments at key decision points and contribute to IND-enabling activities

4. Innovation & Mentorship

Champion new technologies and foster a culture of scientific curiosity and continuous improvement.

Identify and evaluate emerging technologies in antibody engineering and computational design

Mentor junior scientists through technical guidance, hands-on coaching, and career development support

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Qualifications

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.

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We look forward to welcoming a Principal Scientist who will bring both deep bench expertise and computational boldness to advance our antibody engineering capabilities and deliver the next generation of therapeutic molecules for patients.

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