What is a BioAI Software Engineer? The Role I Had to Invent
If I had started learning AI five years ago, or blockchain ten years ago, I’d be standing on the wave by the time it hit.
That thought stuck with me.
I’ve been a software engineer for four years now. I’ve worked in software houses and product companies, written code in Ruby, JavaScript, and React. I’ve learned to build fast, ship reliably, and design with care. But a few months ago, I started asking a bigger question:
Where is the next boom coming?
Not in the “which language should I learn” way. I mean-what’s going to reshape the world the way AI is doing now? What’s the next domain where the smartest minds will gather, the best tools will emerge, and the most complex problems will get solved?
And just like the early whispers of crypto in 2015 or the AI wave that exploded in 2023, I feel something building again. Quietly. Under the surface.
It’s bioengineering.
And AI.
Together.
Why Bio + AI?
Because biology is finally readable.
Proteins. DNA. Cell simulations. Synthetic pathways. All of it is data now-messy, high-dimensional data. And guess what AI is good at? Exactly that.
Large language models can now predict protein folding. Diffusion models are simulating molecular behavior. GANs are generating synthetic chemical structures. The frontier of drug discovery and synthetic biology is becoming an AI problem.
And I want to be part of it.
But what’s the role?
I started looking.
What do you call someone who builds software systems that apply AI to biological data?
Bioinformatics Engineer? Too narrow. Machine Learning Engineer? Too broad. Computational Biologist? Too academic. AI Researcher? Too vague.
None of them felt like me.
So I made up the title I wanted to grow into:
BioAI Software Engineer
It’s not a real title. Yet.
But it captures what I want to become: a developer who uses AI to solve problems in bioengineering and synthetic biology. A software engineer who understands molecules, simulations, pipelines, models - and builds tools at the intersection of these worlds.
What this site is about
This website is my second brain.
I built it to document the journey. Every article, glossary term, roadmap item - everything here is something I’ve either learned, struggled with, or plan to learn.
Because this isn’t a roadmap from expert to expert. It’s from beginner to engineer.
You’ll find posts about:
- foundational biology (enzymes, proteins, CRISPR)
- the math behind AI (gradients, optimizers, tensors)
- molecular simulation tools
- bioinformatics pipelines
- building and training models
- Rust, Python, and scientific computing
...and probably a bunch of failures too.
What skills does a BioAI Software Engineer need?
Here’s what I think I’ll need to master to earn the title:
-> Biology & Chemistry basics
- Proteins, amino acids, DNA/RNA
- Molecular structures and interactions
- Common lab techniques (PCR, CRISPR, etc.)
-> Data Science & ML
- Python, NumPy, pandas, PyTorch
- ML workflows and model training
- Graph neural networks for molecules
-> Bioinformatics
- Sequence alignment, FASTA/FASTQ
- Genomic data formats
- Tools like Biopython, BLAST, BWA
-> Simulation & Visualization
- Molecular dynamics (OpenMM, GROMACS)
- Protein folding (AlphaFold)
- Visualizing molecules in 3D
-> Software Engineering
- Strong programming skills (Python, Rust)
- APIs, pipelines, infrastructure
- Writing clean, tested, reproducible code
I’m not there yet. But that’s what this journey is for.
Why share it?
Because when I was searching for this role, it didn’t exist.
So I want to make the path visible for the next person who’s like me: a curious software engineer who sees the future forming in the biotech labs, research hubs, and deep learning models-and wants in.
If that’s you, welcome.
If you’re already ahead of me, I’d love your feedback.
And if you’re just AI-curious or bio-curious, stick around.
The future of software isn’t just apps and APIs. It’s cells. It’s molecules. It’s evolution-simulated, steered, and scaled by code.
Let’s build it.