Overview

Bioinformatics & AI Engineer

Responsibilities

• Design, develop, validate, and operate foundation-model-enabled applications for genomics, transcriptomics, single-cell and spatial omics, proteomics, imaging, and clinical data.
• Adapt and evaluate biological foundation models, protein and sequence models, multimodal models, and large language models for biomarker discovery, target identification, patient stratification, and scientific decision support.
• Build robust model development workflows spanning data curation, representation learning, fine-tuning or parameter-efficient adaptation, retrieval augmentation, evaluation, and monitored deployment.
• Engineer scalable, reproducible pipelines for preparing and harmonizing multi-omics and clinical datasets, with clear provenance, versioning, quality controls, and fit-for-purpose access controls.
• Develop agentic workflows that combine foundation models with validated bioinformatics tools, structured knowledge, and human review to support research planning, quality control, analysis execution, and result interpretation.
• Define rigorous benchmarking and validation strategies, including biological relevance, robustness, bias assessment, uncertainty, hallucination risk, and reproducibility for models and AI-enabled workflows.
• Partner on real world data projects and establish utility for precision medicine applications
• Partner with computational biology, wet-lab, clinical, data engineering, and product teams to translate scientific needs into usable, well-documented technical solutions.
• Develop production-ready services and interfaces using cloud and GPU infrastructure; optimize performance, cost, reliability, and observability for large-scale data and model workloads.
• Produce clear technical documentation, model cards, evaluation reports, and methods descriptions suitable for internal review, regulated development contexts, and scientific publication.
• Troubleshoot end-to-end platform and pipeline issues, promote engineering best practices, and contribute to a culture of scientific rigor and responsible AI use.

Qualifications

• Strong understanding of genomics, transcriptomics, single-cell or spatial omics, proteomics, imaging, or other biomedical data modalities and their analytical limitations.
• Working knowledge of biological data formats and standards, including FASTQ, BAM/CRAM, VCF/MAF, HDF5, AnnData, Seurat, and metadata best practices.
• Ability to design scientifically meaningful benchmarks and communicate model performance, limitations, uncertainty, and responsible-use guidance to technical and scientific stakeholders.
• Strong statistical reasoning and experience applying quality control and appropriate evaluation methods to biological data and machine learning systems.

Education

• Master’s or PhD in Bioinformatics, Computational Biology, Computer Science, Machine Learning, Statistics, Genetics/Genomics, or a related discipline.
• 7+ years of hands-on experience building bioinformatics, machine learning, data science, or research software solutions; experience applying AI to biomedical or life-science data is strongly preferred.
• Strong programming skills in Python and practical experience with software engineering practices, including Git, testing, code review, CI/CD, and documentation.
• Hands-on expertise with deep learning and foundation models, including transformers, self-supervised learning, embedding models, fine-tuning or parameter-efficient adaptation, evaluation, and inference optimization.