Application Guide

How to Apply for Machine Learning Engineer, Life Sciences

at Goodfire

🏢 About Goodfire

Goodfire appears to be a specialized AI/ML company focused on applying advanced foundation models to life sciences, particularly in genomics and biomedical data. The company likely operates at the intersection of cutting-edge ML research and practical biological applications, suggesting a fast-paced environment where research directly impacts scientific discovery and customer solutions. Working here would appeal to those wanting to bridge ML engineering with meaningful biological impact.

About This Role

This Machine Learning Engineer role involves leading scientific research with partners to interpret biological foundation models (genomic models, ViTs, PLMs), then productionizing that research into tools and APIs. You'll own end-to-end delivery of high-stakes customer projects, from problem definition through implementation, while optimizing interpretability pipelines and infrastructure. The role is impactful because it translates frontier ML research into practical tools that advance life sciences discovery.

💡 A Day in the Life

A typical day might involve collaborating with research scientists to interpret genomic foundation model outputs, then designing and implementing APIs to productionize those insights. You could be optimizing distributed training pipelines for interpretability research while troubleshooting customer deployment issues, balancing immediate project delivery with building maintainable infrastructure.

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🎯 Who Goodfire Is Looking For

  • Has 5+ years in ML infrastructure/research engineering with proven experience deploying and maintaining ML systems at scale
  • Possesses strong expertise in Python, PyTorch/Jax, and distributed systems, with comfort working across research and engineering boundaries
  • Has direct experience with biological/life sciences ML (computational biology, bioinformatics, genomics, or multimodal biomedical data)
  • Can demonstrate experience taking research from concept to production, specifically with model interpretability and tool building

📝 Tips for Applying to Goodfire

1

Highlight specific projects where you've productionized ML research into maintainable tools/APIs, especially for model interpretability

2

Quantify your experience with 'high-stakes projects' and 'whatever it takes' delivery mentality with concrete examples

3

Explicitly connect your biological ML experience to the listed domains (genomics, digital pathology, protein modeling, or multimodal biomedical data)

4

Showcase your distributed systems and infrastructure optimization experience with metrics (scale, performance improvements, etc.)

5

Demonstrate your ability to work across research and engineering boundaries by describing collaborative projects with research scientists

✉️ What to Emphasize in Your Cover Letter

['Your experience interpreting advanced foundation models (specifically mentioning genomic models, ViTs, or PLMs if applicable)', 'Examples of taking interpretability research from concept to production tools/APIs that work on real models and data', 'Your approach to leading scientific research with partners and owning end-to-end project delivery', "Specific biological ML domain expertise and how it applies to Goodfire's focus areas"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Investigate Goodfire's specific focus areas within biological foundation models (genomics, digital pathology, etc.) through any available publications or talks
  • → Research the company's partners/customers in life sciences to understand their high-stakes project context
  • → Look for information about their technical stack and infrastructure approach to distributed ML systems
  • → Understand the competitive landscape of companies applying foundation models to life sciences

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Deep dive into your experience with biological foundation models and interpretability techniques
2 Technical discussion about optimizing pipelines for model interpretability, training, and inference
3 Scenario-based questions about leading research with scientific partners and managing high-stakes customer projects
4 Systems design questions for building maintainable tools/APIs that productionize research findings
5 Questions about your experience with PyTorch/Jax distributed systems and scaling ML infrastructure
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⚠️ Common Mistakes to Avoid

  • Presenting only research experience without demonstrating production deployment and maintenance of ML systems at scale
  • Generic ML experience without specific biological/life sciences applications or foundation model expertise
  • Focusing solely on engineering without showing ability to collaborate on scientific research and interpretability

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

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Good luck with your application to Goodfire!

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