Role Overview
We are seeking a highly capable machine learning researcher with deep expertise in generative modeling. In this role, you will join an interdisciplinary group of machine learning practitioners, scientists, and engineers working together to advance how we design biological systems and develop new therapeutic approaches. You will be responsible for developing novel generative models aimed at creating functional proteins validated in laboratory settings.
Who You Are
- You are an experienced ML researcher with a strong background in generative modeling. You have contributed substantially to major machine learning efforts—such as open-source libraries, significant product deployments, or impactful scientific publications.
- You are an effective ML engineer. You write maintainable, well-tested code, use modern development workflows, and are equally comfortable rapid-prototyping and producing high-quality production systems. You have experience training and running large-scale models on cloud or distributed hardware.
- You have strong data engineering skills. You can build scalable data pipelines for training and evaluating deep learning models, inspect and refine raw data, design appropriate dataset splits, and ensure data systems perform reliably.
- You are deeply motivated by model quality and performance. You understand how frameworks, hardware, and data interact, and you enjoy optimizing model architecture, throughput, and evaluation metrics.
- You are mission-driven, adaptable, and intellectually curious. You thrive in fast-moving environments, stay focused on end goals, and approach problems of all sizes with enthusiasm.
What Sets You Apart
- Experience in computational biology, protein design, or ML applications in the life sciences.
- Academic training or professional exposure to natural sciences such as physics, biology, or chemistry.
Your Responsibilities
Develop machine learning systems with real-world impact (~90%):
- Help curate training and evaluation datasets.
- Define and implement evaluation metrics aligned with practical objectives.
- Rapidly prototype and iterate on generative modeling approaches.
- Collaborate in a shared codebase with colleagues across research and engineering.
- Support the infrastructure used for compute, experimentation, and model development.
- Work with experimental teams to plan laboratory testing and run model inference for biological targets.
Personal and Professional Development (~10%):
- Stay informed about the latest advances in machine learning.
- Develop working knowledge of protein science and cellular biology.
- Participate in internal knowledge-sharing activities.
- Attend relevant scientific or technical events.
What We Offer
- Competitive compensation and benefits
- Retirement contributions
- Generous leave policies, including inclusive parental leave
- Flexible and hybrid working arrangements
- Opportunities for travel and professional development
We provide a collaborative and intellectually stimulating environment, along with the opportunity to influence the future of biological design through state-of-the-art generative modeling. We encourage applicants from all backgrounds and are committed to fostering a diverse and inclusive team.
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