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Last edited by MrOllie (talk | contribs) 9 days ago. (Update) |
A biotech software engineer is a professional role at the intersection of biotechnology, software engineering, and increasingly, artificial intelligence (AI). These engineers design, develop, and maintain software systems that enable research, diagnostics, and production in biotechnology fields such as genomics, proteomics, drug discovery, and bioprocess automation.[1][2]
Core responsibilities and subroles
editBiotech software engineers bridge the gap between laboratory science and computational infrastructure. Common subroles include:
- Bioinformatics software engineer
- Develops pipelines and algorithms for DNA sequencing, RNA sequencing, genome assembly, variant calling, and annotation; works with formats such as FASTQ, BAM/CRAM, and VCF; and builds reproducible workflows.
- Bioprocess automation engineer
- Designs control, monitoring, and data integration software for fermentation and cell-culture processes; integrates sensors, historians, and MES/LIMS; implements traceability and compliance (e.g., 21 CFR Part 11, GxP).
- AI/ML engineer in biotechnology
- Builds predictive models for target discovery, molecular docking, ADMET, image analysis, and omics integration; develops data pipelines and model serving infrastructure.[3]
- Simulation and modeling engineer
- Creates software for molecular dynamics, protein folding, and systems/synthetic biology models; accelerates workloads on GPUs and HPC clusters.
- BioAI software engineer
- Applies foundation models and modern AI to biological sequences, structures, images, and text. Typical focus areas include protein/RNA language models, structure prediction, generative design (proteins, antibodies, small molecules), multimodal omics integration, and LLM tooling for lab workflows.
Competencies and technologies by subrole
edit| Subrole | Domain knowledge | Core tools & standards | Infrastructure / MLOps |
|---|---|---|---|
| Bioinformatics software engineer | NGS, variant biology, genome builds, QC | FASTQ, BAM/CRAM, VCF; GATK; samtools/htslib; bcftools; bedtools; annotation (Ensembl/VEP); workflow engines (Nextflow, Snakemake, Cromwell) | Unix, Git; reproducible envs (conda/mamba); schedulers (SLURM); cloud batch (AWS Batch, GCP Life Sciences) |
| Bioprocess automation engineer | Fermentation kinetics, PAT, DoE, GMP/GxP | LIMS/ELN integration; OPC-UA/MQTT; historians (OSIsoft PI); DoE tools; SCADA/HMI | PLC/edge gateways; MES integration; audit trails; dashboards (Grafana) |
| AI/ML engineer in biotechnology | Omics, imaging, chemoinformatics; model validation | Python; PyTorch/TensorFlow; scikit-learn; RDKit; MONAI; Scanpy | MLflow/W&B; model serving; Kubernetes/Kubeflow; SageMaker |
| Simulation and modeling engineer | Force fields, MD/MC sampling; systems biology models | OpenMM, GROMACS, NAMD; Rosetta; SBML/COPASI | GPU/CUDA; HPC modules; workflow automation; visualization (VMD, PyMOL) |
| BioAI software engineer | Protein/RNA LMs, generative models, retrieval over corpora | PyTorch/JAX; AlphaFold/OpenFold/ESMFold; diffusion/flow models; FAISS/ScaNN | GPU inference, ONNX/TensorRT; RAG pipelines; governance & bias monitoring |
Learning pathways
editStructured curricula outline cross-disciplinary skills (biology, software engineering, and AI) and list widely used tools, datasets, and benchmarks; these resources support self-guided upskilling for new entrants and career transitioners.
See also
editReferences
edit- ^ Chicco, D. (2020). "Ten quick tips for machine learning in computational biology". Bioinformatics. 36 (20): 5404–5410. doi:10.1093/bioinformatics/btaa684.
- ^ Resnik, D. B. (2002). "The Role of Software in Biotechnology". Nature Biotechnology. 20 (10): 1015–1016. doi:10.1038/nbt1002-1015.
- ^ Blanco-Gonzalez, A. (2022). "The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies". arXiv. arXiv:2206.01475.