Principal Scientist, Bioinformatics

Legend Biotech•Somerset, NJ

2d•Onsite

About The Position

Legend Biotech is seeking a highly motivated and innovative Principal Scientist of Bioinformatics to join its multidisciplinary Research & Development team in Somerset, New Jersey. The role focuses on AI for drug discovery and protein engineering, including developing generative AI models and agentic AI systems to discover novel disease targets. It also applies geometric deep learning and structural biology principles to design and refine high-affinity binders, such as scFvs, VHHs, and novel scaffolds, for CAR-T cell therapies. The role involves mining multi-omic datasets, clinical literature, and biological pathways to identify tumor-specific drug targets; evaluating target druggability and potential off-tumor toxicity; developing generative models to design specific binders; modeling immune interfaces and paratope-epitope interactions; and refining binder structures and biophysical properties to support effective CAR-T signaling and help prevent T-cell exhaustion.

Requirements

Ph.D. in Computational Biology, Computer Science, Structural Biology, or Biophysics.
At least 3 years of post-Ph.D. experience, or at least 6 years with an M.S. or B.S., in the biotech or pharmaceutical industry or an advanced research setting focused on computational drug discovery, target identification, and antibody or binder design.
Expert programming proficiency and deep learning framework experience in Python, primarily with PyTorch.
Experience building generative AI models and agentic AI systems using LLM orchestration frameworks to query biomedical databases and literature.
Deep familiarity with structural biology software, including AlphaFold, ESMFold, RFdiffusion, ProteinMPNN, BindCraft, and FoldFlow.
Proficiency with physics-based simulation and structural optimization and visualization tools, such as GROMACS, AMBER, OpenMM, and PyMOL.
Solid understanding of structural immunology.
Expertise in interface biophysics, including calculating interface binding energies (ΔΔG) and evaluating electrostatic surface potentials.
Familiarity with foundational multi-omics platform datasets and target evaluation databases, such as TCGA, Open Targets, Human Protein Atlas, and DepMap.
Applicants must be authorized to work in the United States.

Responsibilities

Architect and deploy agentic AI systems to mine multi-omic datasets, clinical literature, and biological pathways for novel, tumor-specific drug targets.
Build predictive frameworks using transcriptomics and other datasets to evaluate target druggability and potential off-tumor toxicity.
Develop or adapt generative models, including protein language models, diffusion models, and inverse folding algorithms, to design novel, highly specific binders for tumor antigens.
Model immune interfaces using geometric deep learning to predict paratope-epitope interactions, with the goals of achieving high specificity and minimizing off-tumor toxicity.
Inspect AI-designed binders and correct physical anomalies, optimize hydrophobic packing and hydrogen-bonding networks, and resolve steric clashes or backbone strain.
Tune binder biophysical properties to achieve optimal CAR-T signaling and help prevent T-cell exhaustion.

Benefits

Medical, dental, and vision insurance.
401(k) retirement plan with a company match that vests fully on day one.
Performance-based bonus and/or equity for employees in eligible roles.
Eight weeks of paid parental leave after three months of employment.
Paid time off that includes vacation time, personal time, sick time, floating holidays, and eleven company holidays.
Flexible spending and health savings accounts.
Life and AD&D insurance.
Short- and long-term disability coverage.
Legal assistance.
Supplemental pet, critical illness, accident, and hospital indemnity insurance plans.
Voluntary commuter benefits.
Family planning and care resources.
Well-being initiatives.
Peer-to-peer recognition programs.