Welcome to the Bio AI Software Engineer Roadmap - a practical and evolving learning path for developers at the intersection of software engineering, biology, and AI.
Whether you're an AI engineer entering biotech, a bioinformatician diving deeper into ML, or a developer curious about life science tools - this roadmap gives you the real-world skills to build impactful software in biology.
Built and maintained alongside the live roadmap at bioaisoftware.engineer
A Bio AI Software Engineer builds intelligent tools, pipelines, and infrastructure for biological problems - like protein folding, variant prediction, drug discovery, or lab automation. This includes:
- Writing production-ready Python and backend APIs
- Applying ML/AI to biological sequences and images
- Working with bioinformatics pipelines and HPC/cloud compute
- Making research workflows reproducible, interpretable, and scalable
They speak both the language of code and biology - and often translate between worlds.
The roadmap is split into 7 progressive stages, each with hands-on projects and verified learning resources:
| Stage | Description |
|---|---|
| Stage 1 | Programming Foundations (Python, CLI, Git, clean code) |
| Stage 2 | Software Engineering for Data & APIs (FastAPI, SQL, testing) |
| Stage 3 | Data Literacy & ML (stats, sklearn, PyTorch, LLMs) |
| Stage 4 | Biology & Bioinformatics Foundations (DNA, proteins, pipelines) |
| Stage 5 | Bio-AI (Genomics, Proteomics, LLMs for Bio) |
| Stage 6 | Data Engineering & MLOps (Snakemake, DVC, CI/CD, cloud) |
| Stage 7 | Compliance, Reproducibility & Communication |
Each skill has:
- 🔗 Hand-picked courses or docs
- 💻 A real-world project challenge
Browse the interactive roadmap:
👉 bioaisoftware.engineer/roadmap
Start with Stage 1 if you're new to backend development or Python.
Jump into Stage 4+ if you already know biology but want to learn ML or engineering.
- Stage 1: Programming Foundations
- Stage 2: Software Engineering for Data & APIs
- Stage 3: Data Literacy & ML Foundations
- Stage 4: Biology & Bioinformatics Foundations
- Stage 5: Bio-AI (Genomics, Proteomics, Cheminformatics)
- Stage 6: Data Engineering, Pipelines & MLOps
- Stage 7: Compliance, Safety & Communication
Goal: Learn core programming, clean coding practices, Git, Linux, and basic scripting.
| Skill | Topics | Recommended Resources |
|---|---|---|
| Python Core | Types, OOP, typing, venv, packaging | Python Docs |
| Dev Environment | Black, Ruff, logging, .env, structured logging | pre-commit |
| Git & GitHub | Branching, semantic commits, PRs, changelogs | Git Handbook |
| Linux / CLI Basics | Bash, grep/sed/awk, SSH, tmux | LinuxCommand.org |
Goal: Learn to process data efficiently and build robust APIs and services.
| Skill | Topics | Recommended Resources |
|---|---|---|
| Data Analysis | NumPy, Pandas, tidy data, visualization | Kaggle Pandas |
| SQL & Data Access | CTEs, indexing, joins, SQLAlchemy ORM | SQLBolt |
| APIs & FastAPI | FastAPI, Pydantic, OpenAPI, JWT auth | FastAPI Docs |
| Testing & Packaging | pytest, tox, wheels, SemVer | pytest Docs |
Goal: Learn statistical thinking, classic ML, deep learning, and LLM foundations.
| Skill | Topics | Recommended Resources |
|---|---|---|
| Statistics for ML | Hypothesis testing, bootstrapping, effect size | Khan Stats |
| ML (scikit-learn) | Pipelines, metrics, CV, model selection | Kaggle ML |
| Deep Learning (PyTorch) | CNNs, RNNs, transformers, autograd, schedulers | PyTorch |
| LLMs & RAG | Embeddings, retrieval, prompt engineering | HuggingFace NLP |
Goal: Understand the biological data and systems you're working with.
| Skill | Topics | Recommended Resources |
|---|---|---|
| Molecular Biology | DNA/RNA, expression, mutations | Khan Biology |
| Bio Data Formats & Repos | FASTA, FASTQ, BAM, PDB, Ensembl | NCBI Developer |
| Bioinformatics Tools | BLAST, MAFFT, bcftools, VCF | Rosalind |
| Protein Structure | PDB, motifs, UniProt, visualization | PDB 101 |
Goal: Apply AI models to biological sequences, protein structures, and small molecules.
| Skill | Topics | Recommended Resources |
|---|---|---|
| AI for Genomics | Variant effect prediction, embeddings | Hugging Face Spaces |
| Protein Language Models | ProtTrans, ESM, similarity search | Meta ESM |
| Structure Prediction | AlphaFold, OpenFold, pLDDT | AlphaFold DB |
| Cheminformatics | RDKit, SMILES, ADMET, QSAR | DeepChem |
| LLMs for Bio | Tool use, agents, protocol copilot | LangChain Docs |
Goal: Build reproducible, scalable pipelines with versioning and deployment.
| Skill | Topics | Recommended Resources |
|---|---|---|
| Data Engineering | Parquet, ETL, Airflow, Great Expectations | DataTalks Zoomcamp |
| Reproducible Pipelines | Snakemake, Nextflow, containers | Snakemake Docs |
| Experiment Tracking | MLflow, W&B, model registry | MLflow Docs |
| Cloud & HPC | AWS, GCP, SLURM, cost control | SLURM Quick Start |
| Deployment & CI/CD | Docker, GitHub Actions, autoscaling | GitHub Actions Docs |
Goal: Make your work reproducible, ethical, and understandable.
| Skill | Topics | Recommended Resources |
|---|---|---|
| Data Governance | PII, HIPAA, audit trails | NIST Privacy Framework |
| Reproducible Science | FAIR principles, DVC, DOIs | DVC Docs |
| Communication | Visuals, explainability, methods sections | Nature Data Viz |
- Python, Pandas, PyTorch, FastAPI
- scikit-learn, transformers, LangChain
- Docker, GitHub Actions, SQL, Airflow
- Snakemake, Nextflow, Biopython, UniProt, AlphaFold
- LLMs, RAG, FAISS, vector DBs
- DVC, MLflow, RDKit, DeepChem
And more - updated continuously.
Every skill is paired with a small but powerful project:
| Project | Description |
|---|---|
| DNA→Protein Translator | Build a tool that converts DNA to amino acid chains using codon tables |
| Microscopy Image Classifier | Train a CNN to triage cellular image quality |
| Sample Registry API | Serve metadata with FastAPI, JWT auth, and OpenAPI docs |
| Variant Effect Scorer | Use sequence models to rank genomic variants for lab validation |
| Reproducible RNA-seq Pipeline | Build an RNA-seq workflow with Nextflow and containers |
| RAG Assistant for Protocols | QA system over lab protocols with citation-backed answers |
More projects coming soon. All designed for clarity, real-world value, and resume use.
- bioaisoftware.engineer – Main roadmap, articles, visualizer
- biotechsoftware.engineer – community-driven list of biotech software engineers for hire or collaboration for bio/AI/software roles
- Roadmap.sh – Interactive roadmap format
- AlphaFold DB
- UniProt
- NCBI Developer Hub
- FAIR Principles
- MLflow
- DeepLearning.ai Courses
This roadmap is actively maintained.
Please suggest improvements, add quality resources, or flag outdated links by opening an issue.
This project is open and free to use, modify, and remix for non-commercial learning.
For inquiries about collaboration or licensing for teaching/research, contact via the site.
This roadmap was built by engineers, not marketers.
No fluff. Just skills that matter in bio, ML, and software.