Job Description

The Role

We are building the first generation of biological reasoning models—generative AI systems that can decode biology across scales, from molecules to whole organisms. The goal is to predict, understand, and program living systems in ways never before possible.

As a Machine Learning Engineer, you will design and scale deep generative models for biology. You’ll collaborate closely with experienced founders and domain experts to push the boundaries of how AI can be applied in life sciences.

What You’ll Do

• Build foundational models that can “read and write” biology at scale.
• Develop and experiment with generative architectures (transformers, diffusion, autoencoders) to capture complex, multi-scale biological data.
• Work on distributed training pipelines handling billions of parameters across multi-GPU and multi-node environments.
• Engineer efficient data pipelines for massive datasets, optimizing for speed, memory, and reproducibility.
• Design robust evaluation frameworks to ensure integrity, prevent leakage, and validate models against real-world biological problems.

What We’re Looking Fo

• Bachelor’s in Computer Science, Machine Learning, or related technical field.
• 3+ years of experience developing and deploying deep generative models.
• Solid experience in pre-training models and distributed computing environments.
• Proficiency in Python and at least one deep learning framework (PyTorch, TensorFlow, JAX).
• Familiarity with large-scale datasets and scaling models to billions of parameters.
• Strong grasp of ML fundamentals: architectures, optimization, and evaluation.
• Experience designing and managing large-scale data pipelines.
• Background in robust evaluation methods for complex ML projects.
• Strong coding practices: version control, testing, collaborative workflows.

Who You Are

1. Problem-solver who thrives in ambiguity and takes ownership.
2. Clear communicator who can explain complex technical ideas.
3. Motivated to make a real-world impact through AI in life sciences.
4. Pragmatic and focused, but curious enough to test unconventional ideas.

Bonus Points

• Experience applying ML to biology or chemistry.
• Publications or open-source contributions in ML/AI.
• High-performance computing and ML Ops experience.

Culture & Values

❤️ Ownership and pride in your work.

? Commitment to excellence and high standards.

? Practical, results-oriented mindset.

? Honest and transparent communication.

? Belief that work should also be fun and rewarding.

What’s Offered

• Competitive salary and meaningful equity.
• Medical, dental, and vision coverage.
• A culture of feedback and growth: leadership sets high expectations, shares constructive input, and welcomes ideas from every team member.
• Freedom to manage your day-to-day while hitting key milestones.
• A chance to shape the culture and direction of the team from an early stage.

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