Machine Learning Scientist - Generative AI for Protein Design

We are seeking a creative and rigorous Machine Learning Scientist to develop next-generation generative models for protein and biologics design. The scientist will conduct original research at the intersection of generative AI, geometric deep learning, protein language modeling, and structure-based molecular design, with particular emphasis on diffusion or flow-based models, large protein language models, and E(3)/SE(3)-equivariant architectures. The successful candidate will formulate biologically meaningful design problems, develop and train new models across protein sequence and three-dimensional structure, and translate model outputs into experimentally testable hypotheses. Working in close partnership with antibody/protein engineers, structural biologists, biophysicists, data scientists, and ML engineers, this individual will help establish closed-loop design-make-test-learn workflows for biologics discovery. Essential Responsibilities: • Develop novel generative ML methods for protein sequence, backbone, side-chain, complex, and interface design using diffusion, score-based, flow-matching, autoregressive, masked, or hybrid approaches. • Design and implement E(3)/SE(3)-equivariant or invariant neural architectures that respect biomolecular geometry, including graph neural networks, geometric transformers, frame-based models, and related methods. • Build and adapt protein language models and multimodal foundation models that jointly reason over sequence, structure, function, text, assay, and property data. • Create conditional generation strategies for target-aware binder design, motif scaffolding, inverse folding, interface optimization, antibody or protein engineering, and multi-parameter property optimization. • Integrate biological and physical constraints into generation, including symmetry, residue or motif constraints, sterics, geometry, energetics, developability, and manufacturability considerations. • Develop robust training datasets and data pipelines from structural, sequence, assay, and literature-derived sources; establish sound split strategies that minimize leakage and accurately assess generalization and novelty. • Define fit-for-purpose evaluation frameworks covering structural plausibility, sequence recovery, diversity, novelty, target interaction, uncertainty, computational efficiency, and prospective experimental success. • Use structure prediction, inverse folding, docking, molecular modeling, and physics-based tools to triage and refine generated candidates while recognizing the limitations of in silico scoring. • Partner with experimental scientists to select candidates, interpret assay results, learn from failures, and iteratively improve models through design-make-test-learn cycles or active learning. • Conduct ablation studies, benchmarking, error analysis, and mechanistic investigation to understand model behavior and establish scientific credibility. • Publish high-impact research, contribute to patents where appropriate, and communicate methods and results clearly to scientific and project teams. • Write reproducible, well-tested research code and collaborate with ML engineering and MLOps colleagues to scale training, inference, model tracking, and deployment. • Monitor advances in generative modeling and computational protein design; translate promising ideas into practical capabilities for biologics programs. Qualifications Required: • Master's degree or Ph.D. with research or industry experience in machine learning, computational protein science, structural bioinformatics, geometric deep learning, generative modeling, or a closely related area. Candidates with a Master's degree should typically bring substantial hands-on research or industry experience and a demonstrated record of technical contributions. • Demonstrated hands-on experience developing generative models, with depth in at least two of the following: diffusion/score-based models, flow matching, autoregressive or masked language models, variational methods, energy-based models, or reinforcement learning for molecular design. • Strong experience with geometric deep learning and three-dimensional molecular representations, including E(3)/SE(3)-equivariant models, graph neural networks, geometric transformers, local frames, rotations, or manifold-aware learning. • Experience with protein language models or biological foundation models and an understanding of protein sequence-structure-function relationships. • Strong knowledge of modern deep-learning methods, including transformers, attention, representation learning, conditional generation, sampling, fine-tuning, and uncertainty or calibration. • Proficiency in Python and PyTorch or JAX, with the ability to independently implement, train, debug, and evaluate research models on multi-GPU systems. • Experience working with protein sequence and structure data and common formats or resources; practical familiarity with structure prediction and protein-design workflows. • A rigorous approach to dataset construction, leakage control, benchmark design, statistical analysis, reproducibility, and interpretation of negative results. • A strong research record demonstrated through first-author or substantial-contribution publications at leading ML or computational biology venues, impactful open-source work, patents, or equivalent evidence. • Excellent scientific communication and collaboration skills, including the ability to work across ML, computational, and experimental disciplines. • Professional working proficiency in English; Chinese communication capability is strongly preferred for the Shanghai-based team. Preferred Qualifications: • Direct experience with protein or antibody generation systems such as RFdiffusion-family methods, Chroma, ProteinMPNN/LigandMPNN, ESM-family models, ProGen, diffusion protein language models, or comparable internal methods. • Experience designing binders, antibodies, enzymes, peptides, protein complexes, or constrained functional motifs, ideally with prospective experimental validation. • Familiarity with antibody-specific challenges such as CDR representation, antigen conditioning, affinity/specificity optimization, human-likeness, immunogenicity, aggregation, stability, and other developability properties. • Experience with multimodal or all-atom modeling, co-design of sequence and structure, preference optimization, active learning, Bayesian optimization, or lab-in-the-loop learning. • Knowledge of molecular modeling or structural bioinformatics tools such as AlphaFold, Rosetta, OpenFold, molecular docking, or molecular dynamics. • Experience training large models with distributed computing and mixed precision; familiarity with experiment tracking and reproducible ML workflows. • AWS cloud-based ML experience is an asset, particularly GPU training, S3 data workflows, containers, SageMaker, AWS Batch, EKS, or related services. 百济神州全球胜任力 当我们通过以下十二项全球胜任力，展现出 "患者为先"、"无界协作"、"锐意创新 "和 "追求卓越 "的价值观时，我们就能帮助全世界更多患者获得更多负担得起的药品。 ●团队协作 ●提供并征求坦诚及可行的反馈 ●自我认知 ●兼容并蓄 ●积极主动 ●开拓精神 ●持续学习 ●拥抱变化 ●结果导向 ●分析性思维/数据分析 ●卓越财务 ●清晰沟通 BeOne Global Competencies When we exhibit our values of Patients First, Collaborative Spirit, Bold Ingenuity and Driving Excellence, through our twelve global competencies below, we help get more affordable medicines to more patients around the world. ●Fosters Teamwork ●Provides and Solicits Honest and Actionable Feedback ●Self-Awareness ●Acts Inclusively ●Demonstrates Initiative ●Entrepreneurial Mindset ●Continuous Learning ●Embraces Change ●Results-Oriented ●Analytical Thinking/Data Analysis ●Financial Excellence ●Communicates with Clarity 求职者隐私申明： 百济神州致力于尊重和保护您的个人信息权利，并承诺依据合法、正当、必要和诚信的原则处理您的个人信息（包括个人敏感信息 ）。 由于百济神州在全球范围内开展业务，我们可能需要基于人力资源管理等合理业务目的而将您的个人信息发送和/或存储在位于您所在国家以外其他国家（例如：美国）的服务器和数据库中，详情参见百济神州《求职者隐私政策》（百济神州官网 - 隐私政策 - 求职者隐私政策）。 如您主动向我们提供您的简历信息或其他个人信息，则视为您已经充分理解并确认接受百济神州《求职者隐私政策》内容。如您对此有任何疑问的，请勿提交简历信息或其他个人信息。 BeOne is committed to respect and protect your personal information rights, and will process your personal information, including your sensitive personal information, based on the principles of legality, legitimacy, necessity, and integrity. Due to the reasonable business need for human resource management as a result of BeOne’s global operation, your personal information may be transferred and/ or stored in a server/database located in a third country (e.g., the United States) other than your own country. For further details, please refer to BeOne Job Applicant Privacy Policy (BeOne official website - Privacy Policy - Job Applicant Privacy Policy). If you voluntarily provide your resume or other personal information to us, it is deemed as you have thoroughly acknowledged and accepted BeOne Job Applicant Privacy Policy. If you have any concern, please DO NOT submit your resume or any other personal information.