Bioinformatics Specialist

Job Title: Bioinformatics - Manager-C Employment Type: 12 Month contract Location: Bridgewater, New Jersey, 08807 Work Location: Onsite Description: The AI Computational Biologist will be a key contributor in developing and applying AI models for target discovery, mechanism elucidation, and drug repurposing, while integrating outputs with wet-lab validation and preclinical research. You’ll collaborate across disciplines — from ML engineers building/working with foundation models to biologists running assays — ensuring that computational insights translate into tangible therapeutic hypotheses. This role is ideal for someone who combines deep biological expertise with fluency in modern AI architectures, and who’s passionate about leveraging LLMs and GNNs to accelerate translational discovery. AI & Computational Modeling Design, train, and implement LLM- and GNN-based models to extract biological relationships from multi-modal data (omics, literature, chemistry, clinical). Integrate knowledge graphs and structured biomedical databases to support hypothesis generation for novel targets and mechanisms. Collaborate with ML teams to fine-tune and evaluate models on domain-specific tasks such as gene–disease association, pathway prediction, and compound efficacy modeling. Skills: Proven experience in target identification and translational discovery — from in silico analysis to preclinical validation. Strong understanding of molecular biology, pharmacology, and disease biology. Hands-on experience developing or applying AI/ML models to biological problems, especially LLMs, GNNs, or multi-modal integration architectures. Prior involvement in wet-lab collaboration (assay design, data interpretation, or experimental validation) preferred. Technical Skills Programming: Expert in Python (pandas, PyTorch, TensorFlow, scikit-learn, Hugging Face, PyTorch Geometric). AI/ML Expertise: Proficiency in LLMs, GNNs, transformers, and model fine-tuning workflows. Bioinformatics Tools: Familiar with databases such as Ensembl, UniProt, ChEMBL, DrugBank, GEO, and OMIM. Data Integration: Experience with multi-omics data fusion and biomedical knowledge graphs. Visualization & Communication: Skilled in building interpretable visualizations and clearly communicating computational findings. Version Control: Proficient in Git and collaborative coding practices. Familiarity with molecular modeling, chemoinformatic, or AI for protein–ligand interaction prediction. Experience in biomedical NLP, scientific literature mining, or ontology construction. Understanding of preclinical pharmacology or toxicogenomic. Experience working in cloud environments (GCP, AWS). Soft Skills Deep curiosity and excitement about connecting AI architectures with biological meaning. Excellent cross-disciplinary communication — able to converse equally well with AI engineers and biologists. Self-directed, detail-oriented, and comfortable working in a fast-paced, dynamic startup environment. Passionate about improving patient outcomes through innovative science and technology. Education: PhD or MS with 5+ years of relevant experience in Computational Biology, Bioinformatics, Systems Biology, Computer Science, or a related discipline.