https://github.com/babilonczyk/bio-ai-software-engineering-roadmap
Explore roadmap to becoming a Bio AI Software Engineer - combining machine learning, bioinformatics, and software engineering to build the future of biotechnology. Join the journey on GitHub! ✨
https://github.com/babilonczyk/bio-ai-software-engineering-roadmap
ai-in-biology artificial-intelligence bioai bioinformatics data-science deep-learning drug-discovery fastapi genomics machine-learning protein-design python roadmap
Last synced: 4 months ago
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Explore roadmap to becoming a Bio AI Software Engineer - combining machine learning, bioinformatics, and software engineering to build the future of biotechnology. Join the journey on GitHub! ✨
- Host: GitHub
- URL: https://github.com/babilonczyk/bio-ai-software-engineering-roadmap
- Owner: babilonczyk
- Created: 2025-07-28T11:31:25.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-08-16T17:46:36.000Z (5 months ago)
- Last Synced: 2025-08-16T19:18:05.764Z (5 months ago)
- Topics: ai-in-biology, artificial-intelligence, bioai, bioinformatics, data-science, deep-learning, drug-discovery, fastapi, genomics, machine-learning, protein-design, python, roadmap
- Homepage: https://www.bioaisoftware.engineer/roadmap
- Size: 31.3 KB
- Stars: 3
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🧬 Bio AI Software Engineer Roadmap
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**](https://bioaisoftware.engineer)
---
## 🧠 What is a Bio AI Software 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.
---
## 📚 Roadmap Structure
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
---
## 🚀 Getting Started
Browse the interactive roadmap:
👉 [bioaisoftware.engineer/roadmap](https://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.
---
## 📌 Table of Contents
- [Stage 1: Programming Foundations](#stage-1-programming-foundations)
- [Stage 2: Software Engineering for Data & APIs](#stage-2-software-engineering-for-data--apis)
- [Stage 3: Data Literacy & ML Foundations](#stage-3-data-literacy--ml-foundations)
- [Stage 4: Biology & Bioinformatics Foundations](#stage-4-biology--bioinformatics-foundations)
- [Stage 5: Bio-AI (Genomics, Proteomics, Cheminformatics)](#stage-5-bio-ai-genomics-proteomics-cheminformatics)
- [Stage 6: Data Engineering, Pipelines & MLOps](#stage-6-data-engineering-pipelines--mlops)
- [Stage 7: Compliance, Safety & Communication](#stage-7-compliance-safety--communication)
## ✅ Stage 1: Programming Foundations
**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](https://docs.python.org/3/) |
| Dev Environment | Black, Ruff, logging, .env, structured logging | [pre-commit](https://pre-commit.com/) |
| Git & GitHub | Branching, semantic commits, PRs, changelogs | [Git Handbook](https://guides.github.com/introduction/git-handbook/) |
| Linux / CLI Basics | Bash, grep/sed/awk, SSH, tmux | [LinuxCommand.org](http://linuxcommand.org/) |
## 📊 Stage 2: Software Engineering for Data & APIs
**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](https://www.kaggle.com/learn/pandas) |
| SQL & Data Access | CTEs, indexing, joins, SQLAlchemy ORM | [SQLBolt](https://sqlbolt.com/) |
| APIs & FastAPI | FastAPI, Pydantic, OpenAPI, JWT auth | [FastAPI Docs](https://fastapi.tiangolo.com/) |
| Testing & Packaging | pytest, tox, wheels, SemVer | [pytest Docs](https://docs.pytest.org/) |
## 🤖 Stage 3: Data Literacy & ML Foundations
**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](https://www.khanacademy.org/math/statistics-probability) |
| ML (scikit-learn) | Pipelines, metrics, CV, model selection | [Kaggle ML](https://www.kaggle.com/learn/intro-to-machine-learning) |
| Deep Learning (PyTorch) | CNNs, RNNs, transformers, autograd, schedulers | [PyTorch](https://pytorch.org/tutorials/) |
| LLMs & RAG | Embeddings, retrieval, prompt engineering | [HuggingFace NLP](https://huggingface.co/learn/nlp-course) |
## 🧬 Stage 4: Biology & Bioinformatics Foundations
**Goal:** Understand the biological data and systems you're working with.
| Skill | Topics | Recommended Resources |
|---------------------------|---------------------------------------------------------------|------------------------|
| Molecular Biology | DNA/RNA, expression, mutations | [Khan Biology](https://www.khanacademy.org/science/biology) |
| Bio Data Formats & Repos | FASTA, FASTQ, BAM, PDB, Ensembl | [NCBI Developer](https://www.ncbi.nlm.nih.gov/home/develop/) |
| Bioinformatics Tools | BLAST, MAFFT, bcftools, VCF | [Rosalind](https://rosalind.info/) |
| Protein Structure | PDB, motifs, UniProt, visualization | [PDB 101](https://pdb101.rcsb.org/) |
## 🧪 Stage 5: Bio-AI (Genomics, Proteomics, Cheminformatics)
**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](https://huggingface.co/spaces?search=genomics) |
| Protein Language Models | ProtTrans, ESM, similarity search | [Meta ESM](https://github.com/facebookresearch/esm) |
| Structure Prediction | AlphaFold, OpenFold, pLDDT | [AlphaFold DB](https://alphafold.ebi.ac.uk/) |
| Cheminformatics | RDKit, SMILES, ADMET, QSAR | [DeepChem](https://deepchem.io/) |
| LLMs for Bio | Tool use, agents, protocol copilot | [LangChain Docs](https://python.langchain.com/) |
## ⚙️ Stage 6: Data Engineering, Pipelines & MLOps
**Goal:** Build reproducible, scalable pipelines with versioning and deployment.
| Skill | Topics | Recommended Resources |
|---------------------------|---------------------------------------------------------------|------------------------|
| Data Engineering | Parquet, ETL, Airflow, Great Expectations | [DataTalks Zoomcamp](https://github.com/DataTalksClub/data-engineering-zoomcamp) |
| Reproducible Pipelines | Snakemake, Nextflow, containers | [Snakemake Docs](https://snakemake.readthedocs.io/) |
| Experiment Tracking | MLflow, W&B, model registry | [MLflow Docs](https://mlflow.org/docs/latest/index.html) |
| Cloud & HPC | AWS, GCP, SLURM, cost control | [SLURM Quick Start](https://slurm.schedmd.com/quickstart.html) |
| Deployment & CI/CD | Docker, GitHub Actions, autoscaling | [GitHub Actions Docs](https://docs.github.com/actions) |
## 🧾 Stage 7: Compliance, Safety & Communication
**Goal:** Make your work reproducible, ethical, and understandable.
| Skill | Topics | Recommended Resources |
|---------------------------|---------------------------------------------------------------|------------------------|
| Data Governance | PII, HIPAA, audit trails | [NIST Privacy Framework](https://www.nist.gov/privacy-framework) |
| Reproducible Science | FAIR principles, DVC, DOIs | [DVC Docs](https://dvc.org/doc) |
| Communication | Visuals, explainability, methods sections | [Nature Data Viz](https://www.nature.com/collections/gjcfhifjhc) |
---
## ✅ Technologies Covered
- 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.
---
## 🔬 Example Project Challenges
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.
---
## 🌐 Tools & Links
- [bioaisoftware.engineer](https://bioaisoftware.engineer) – Main roadmap, articles, visualizer
- [biotechsoftware.engineer](https://biotechsoftware.engineer) – community-driven list of biotech software engineers for hire or collaboration for bio/AI/software roles
- [Roadmap.sh](https://roadmap.sh/bio-ai-software-engineer) – Interactive roadmap format
- [AlphaFold DB](https://alphafold.ebi.ac.uk/)
- [UniProt](https://www.uniprot.org/)
- [NCBI Developer Hub](https://www.ncbi.nlm.nih.gov/home/develop/)
- [FAIR Principles](https://www.go-fair.org/fair-principles/)
- [MLflow](https://mlflow.org/docs/latest/index.html)
- [DeepLearning.ai Courses](https://www.deeplearning.ai/short-courses/)
---
## 🤝 Contributing
This roadmap is actively maintained.
Please suggest improvements, add quality resources, or flag outdated links by opening an issue.
---
## 📄 License
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._