Nathan C. Frey, PhD - CTO at Coefficient Bio

Nathan C. Frey, PhD

CTO & Co-Founder at Coefficient Bio. Building for the future in AI-native biotech. Ex-Prescient Design • Genentech.

Getting Started in BioML Research & Engineering

3 minute read

Published:

Executive Summary

Breaking into AI for Bio takes 1-5 years: solid fundamentals, meaningful research experience, demonstrated execution, and strong engineering practices. Pedigree matters less than what you’ve built and shipped.

Key Insights for Aspiring AI for Bio Researchers

  • Foundation over specialization: Study CS, Applied Math, and physical sciences. Compete on fundamentals, absorb biology through enthusiasm and papers. “Biology is more a question of enthusiasm and fear than of knowledge.”

  • Velocity beats perfection: Iteration speed dominates in empirical research. Complete one year-long project with clear outcomes. Half-finished prototypes teach you less.

  • Engineering discipline multiplies impact: Well-organized, documented code enables collaboration. Messy implementations block progress. Engineers won’t fix your code for you.

  • High agency separates candidates: Identify meaningful problems independently. Solve them proactively. Top researchers don’t wait for assignments.

  • Network before you need to: Build relationships with researchers and hiring managers through conferences and cold emails. Start before job searching.

Who This Guide Is For

  • Undergraduate and graduate students considering AI for bio careers
  • Postdocs and early-career researchers transitioning into AI for bio
  • Job seekers exploring startups, biotechs, or pharmaceutical companies
  • Self-taught individuals supplementing formal education
  • Bootcamp graduates looking to enter the field

What Hiring Managers Actually Look For

When I review candidates for AI for bio positions, I look for three things:

  1. Evidence of completed work - Did you finish something meaningful?
  2. Problem-solving agency - Can you identify and solve problems independently?
  3. Collaborative potential - Can you work effectively with biologists, chemists, and engineers?

Pedigree matters less than execution. I explicitly welcome candidates from underrepresented backgrounds and value diverse perspectives.

Application Materials That Matter

Essential:

  • Writing sample - Research proposal or technical explanation demonstrating clear thinking
  • GitHub repository - Clean, documented code showing engineering quality
  • Published work - Paper, preprint, or substantial technical blog post

Less Important:

  • Prestigious university name
  • Perfect GPA
  • Extensive biology coursework (for computational roles)

Core Fundamentals (6-12 months)

  • Machine Learning: Kyunghyun Cho’s 2025 ML Lecture Notes, Kevin Patrick Murphy’s Probabilistic Machine Learning
  • Biology: Enthusiasm + reading papers > formal coursework
  • Programming: Python, version control (Git), testing, documentation
  • Math: Linear algebra, probability, optimization

Practical Skills (12-24 months)

  • Cheminformatics: Pat Walter’s Practical Cheminformatics tutorials
  • Protein modeling: AlphaFold, ESMFold, structure prediction tools
  • Data analysis: Pandas, visualization, statistical testing
  • Scientific computing: Jupyter notebooks, experiment tracking, reproducibility

Research Experience (12-36 months)

  • Publish a project: Start to finish, with results and writeup
  • Collaborate: Work with experimentalists or other computational scientists
  • Present your work: Conferences, lab meetings, blog posts

The Reality Check

This takes 1-5 years. Long-term career investment. The world needs your contribution - more important problems than people to solve them.

Practical Next Steps

  1. Pick one foundational resource and work through it completely
  2. Identify one research problem you find interesting
  3. Build one small project demonstrating your skills
  4. Share your work (GitHub, blog post, preprint)
  5. Attend one conference and introduce yourself to researchers
  6. Send cold emails to 5-10 researchers whose work you admire

Don’t wait for permission. Start building.

Full article: Read on Substack

Last updated: May 2025

Keywords: AI for bio careers, AI for bio jobs, machine learning biology, biotech hiring, how to get into AI for bio, AI for bio research guidance, TechBio careers, protein engineering careers

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