Bioinformatics Manager

Position Overview

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.

Biological Interpretation & Target Discovery

• Apply AI-driven insights to identify, prioritize, and validate new drug targets and therapeutic hypotheses.
• Design in silico analyses to support mechanism-of-action elucidation, biomarker discovery, and patient stratification .
• Collaborate with wet-lab teams to translate computational predictions into experimental designs , ensuring seamless handoff between in silico and in vitro/in vivo validation.

Data Integration & Curation

• Integrate large-scale datasets from public and proprietary sources (e.g., transcriptomics, proteomics, CRISPR screens, literature corpora).
• Curate structured datasets for LLM fine-tuning, knowledge graph expansion, and GNN training.

Collaboration & Cross-Functional Impact

• Partner with drug discovery , data science , and AI engineering teams to align modeling objectives with biological relevance.
• Contribute to multi-disciplinary project teams driving programs from discovery through preclinical proof-of-concept .
• Communicate computational findings clearly to both scientific and non-technical stakeholders.

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 , chemoinformatics , or AI for protein–ligand interaction prediction .
• Experience in biomedical NLP , scientific literature mining, or ontology construction.
• Understanding of preclinical pharmacology or toxicogenomics .
• 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 .

Pay ranges between 95-107/hr based on experience