R&D – Intrepide – Principal Scientist I, Antibody Engineering – SZ Full-time NEW

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.

Key Responsibilities

• 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
• 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
• 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
• 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

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.