Machine Learning Specialist Generative Models & Protein Folding

About Us:

Grafton Biosciences is a stealth-mode, San Francisco-based biotech startup focused on solving disease through groundbreaking innovations in early detection and therapeutics. We are combining breakthroughs in synthetic biology, machine learning, and manufacturing to fundamentally extend healthy human lifespans. We're looking for passionate team members who want to shape the future.

Role Summary:

We are seeking a highly skilled Machine Learning Specialist with a strong focus on generative models and their application to biological systems, particularly biomolecule folding. The successful candidate will develop, implement, and deploy advanced machine learning algorithms to predict, analyze, and generate molecular structures and properties. This role involves close collaboration with computational chemists and HPC experts to drive our research and development efforts.

Key Responsibilities:

Develop, train, and validate generative machine learning models (e.g., diffusion models, GANs, VAEs) for applications in protein/nucleic acid science and molecular design.

Implement and refine models for protein/nucleic acid structure prediction, potentially leveraging or extending architectures like AlphaFold.

Apply and advance equivariant graph machine learning techniques for molecular data representation and analysis.

Develop and implement robust uncertainty quantification methods (e.g., Gaussian processes, Bayesian approaches) for model predictions.

Manage and process large-scale biological datasets for model training and evaluation.

Utilize cloud computing platforms (Azure/AWS) for computationally intensive model training and deployment.

Collaborate with computational chemists to integrate ML models with molecular simulation data and workflows.

Stay abreast of cutting-edge research in generative models, geometric deep learning, and their scientific applications.

Document research, contribute to publications, and present findings to technical and non-technical audiences.

Essential Qualifications:

Ph.D. or Master's degree in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field. (Equivalent practical experience will be considered in lieu of a formal degree, particularly with a strong portfolio of relevant projects).

Proven experience in developing and applying generative models (e.g., diffusion models, GANs, VAEs).

Strong understanding and practical experience with machine learning for protein folding (e.g., familiarity with models like AlphaFold).

Proficiency in Python and deep learning frameworks such as PyTorch (preferred) or TensorFlow.

Experience with graph neural networks (GNNs), particularly equivariant architectures.

Knowledge of uncertainty quantification techniques in machine learning.

Proficiency in Linux environments and Bash scripting.

Experience with cloud computing platforms (Azure or AWS) for machine learning workloads.

Excellent problem-solving abilities and a strong analytical mindset.

Effective communication and collaboration skills.

Preferred Qualifications:

Experience with reinforcement learning (RL) and its application to scientific discovery.

Familiarity with molecular biology, biochemistry, or cheminformatics.

Track record of publications in relevant machine learning or computational science venues.

Experience in deploying machine learning models into production or research pipelines.

What We Offer:

Competitive compensation.

Comprehensive health, dental and vision coverage.

Opportunity to define a new therapeutic design paradigm and see your work progress through the clinic.