Computational Biologist & AI Engineer – Vaccines

🚨 Now Hiring: Computational Biologist & AI Engineer – Vaccines

📍 Location: Pearl River, NY | Hybrid

📅 Duration: 12 months contract

🎓 Education: Master’s or Ph.D. in Computational Biology, Bioinformatics, Computer Science, AI/ML, Data Science, Immunology, or a related field

We are seeking a Computational Biologist & AI Engineer to support cutting-edge vaccine research through multi-omics analysis, computational immunology, and AI-enabled research workflows.

🔬 Key Responsibilities

• Analyze and integrate multi-omics datasets including bulk RNA-seq, single-cell RNA-seq, TCR/BCR sequencing, proteomics, metabolomics, and immune profiling

• Identify biomarkers and molecular signatures associated with vaccine response, efficacy, and safety

• Develop computational workflows for biological and immune-response analysis

• Design and implement AI-agent solutions to help researchers search, integrate, analyze, and interpret complex biological datasets

• Build LLM/RAG, knowledge systems, APIs, and data pipelines connecting biological datasets, scientific literature, and research workflows

• Collaborate with computational biologists, data scientists, and software engineers to develop scalable research solutions

✅ Required Qualifications

• Master’s or Ph.D. in a relevant quantitative/scientific discipline

• Experience with multi-omics and biomedical datasets

• Strong experience with RNA-seq and/or single-cell sequencing analysis

• Experience with biomarker discovery, immune-response analysis, or systems immunology

• Strong Python programming skills

• Excellent communication and cross-functional collaboration skills

⭐ Preferred Qualifications

• Vaccine, infectious disease, or immunology research experience

• Hands-on experience generating insights from single-cell sequencing data

• Experience with AI/ML, LLMs, RAG, agentic AI, or multi-agent systems

• Experience building LLM-based applications or autonomous research assistants

• Experience with APIs, cloud platforms, databases, knowledge graphs, and bioinformatics pipelines

• Familiarity with modern AI development frameworks and reproducible software practices

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