Lead-Machine Learning Engineer AI For R&D

Key Responsibilities

• Develop and deploy AI/ML models for vaccine, antibody, and drug delivery platform development, formulation optimization, prophylactic and therapeutic design.
• Build in-silico and computational models for:
• Vaccine, antibody, and drug delivery
• Adjuvant development and design
• LNP/lipid nanoparticle and mRNA delivery optimization
• Sequence and construct design
• Gene editing system optimization
• CAR-T construct and cell engineering support
• Biomarker prediction and target prioritization
• Apply predictive modeling, generative AI, and optimization algorithms to improve efficacy, stability, manufacturability, and safety.
• Partner with R&D scientists to translate biological and formulation challenges into AI-driven solutions.
• Support AI infrastructure, model deployment, automation, and scalable data pipelines for R&D.

Qualifications

• MS/PhD in Machine Learning, Computer Science, AI, Computational Biology, Bioengineering, or related field.
• 5-8+ years of experience in AI/ML, computational modeling, or pharmaceutical/biotech R&D.

Technical Skills

• Strong expertise in machine learning, deep learning, optimization, and generative AI (transformer architectures, diffusion models, Variational Autoencoders (VAEs), sequence/structure generative models).
• Proficiency in Python, ML frameworks (PyTorch, TensorFlow, Scikit-learn), and scientific computing.
• Experience in computational modeling, in-silico protein design, simulation, or optimization platforms.
• Experience fine-tuning or adapting foundation/protein language models (e.g., ESM, AlphaFold-class, RFdiffusion, Autoregressive Protein Language Models (pLMs)) for sequence and structure design preferred.
• Familiarity with vaccines, prophylactic antibodies, drug delivery systems, mRNA therapeutics, gene editing, CAR-T, or biologics development preferred.
• Expertise in structural biology and protein design.
• Experience with cloud computing, MLOps, APIs, and workflow automation, including model deployment, versioning, and monitoring (e.g., MLflow, Kubeflow, or equivalent).