Introduction

I love the internet memos like the one from Jeff Bezos about APIs and Marc Andreessen’s 2011 prediction that “software is eating the world.” Over a decade later, it’s devoured more than commerce and media—it’s now eating science, and quite frankly, it’s about time.

Scientific research, especially in domains like biology, chemistry, and medicine, has historically been a software backwater. Experiments were designed in paper notebooks, data handled via Excel, and results shared through PowerPoint screenshots. It’s only recently that leading institutions began embedding software engineering at the core of how science gets done. And the results speak for themselves. The Nobel Prize in Chemistry 2024, awarded for the use of AlphaFold in solving protein structures, is a striking example of how software—developed and scaled by engineers—has become as fundamental to scientific breakthroughs as any wet-lab technique.

The Glue That Holds Modern Science Together

Software engineers aren’t just building tools. At institutions like the Broad Institute, Allen Institute, and EMBL-EBI, they’re building scientific platforms. Terra, Code Ocean, Benchling—these aren’t developer toys, they’re scientific instruments. They standardize experimentation, automate reproducibility, and unlock collaboration at scale.

The Broad Institute’s Data Sciences Platform employs over 200 engineers supporting a staff of 3,000. Recursion Pharmaceuticals operates with an almost 1:1 engineer-to-scientist ratio. These are not exceptions—they’re exemplars.

The Real Payoff: Research Acceleration

When you embed software engineers into scientific teams, magic happens:

  • Setup time drops by up to 70%
  • Research iteration speeds triple
  • Institutional knowledge gets preserved, not lost in SharePoint folders
  • AI becomes usable beyond ChatGPT prompts—supporting actual data analysis, modeling, and automation

These are not hypothetical. They’re documented results from public case studies and internal programs at peer institutions.

From Hype to Hypothesis

While many institutions obsess over full lab digitization (think IoT pipettes), the smarter move is prioritizing where digital already exists: in workflows, data, and knowledge. With tools like Microsoft Copilot, OpenAI Enterprise, and AI language models for genomics like Evo2, AlphaFold for protein structure prediction, and DeepVariant for variant calling—tools that only become truly impactful when integrated, orchestrated, and maintained by skilled engineers who understand both the research goals and the computational landscape, researchers are now unlocking years of buried insights and accelerating modeling at scale.

Scientific software engineers are the missing link. Their work turns ad hoc experiments into reproducible pipelines. Their platforms turn pet projects into institutional capability. And their mindset—rooted in abstraction, testing, and scalability—brings scientific rigor to the scientific process itself.

What many underestimate is that building software—like conducting experiments—requires skill, discipline, and experience. Until AI is truly capable of writing production-grade code end-to-end (and it’s not—see Speed vs. Precision in AI Development), we need real software engineering best practices. Otherwise, biology labs will unknowingly recreate decades of software evolution from scratch—complete with Y2K-level tech debt, spaghetti code, and glaring security gaps.

What Now?

If you’re in research leadership and haven’t staffed up engineering talent, you’re already behind. A 1:3–1:5 engineer-to-scientist ratio is emerging as the new standard—at least in data-intensive fields like genomics, imaging, and molecular modeling—where golden-path workflows, scalable AI tools, and reproducible science demand deep software expertise.

That said, one size does not fit all. Theoretical physics or field ecology may have very different needs. What’s critical is not the exact ratio, but the recognition that modern science needs engineering—not just tools.

There are challenges. Many scientists weren’t trained to work with software engineers, and collaboration across disciplines takes time and mutual learning. There’s also a cultural risk of over-engineering—replacing rapid experimentation with too much process. But when done right, the gains are exponential.

Science isn’t just done in the lab anymore—it’s done in GitHub. And the sooner we treat software engineers as core members of scientific teams, not as service providers, the faster we’ll unlock the discoveries that matter.

Let’s stop treating software like overhead. It’s the infrastructure of modern science.