ML Research Scientist, Co-Folding and Affinity

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SandboxAQ

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2021

🤖 Artificial Intelligence

🔒 Cybersecurity

💊 Pharmaceuticals

Artificial Intelligence • Cybersecurity • Pharmaceuticals

SandboxAQ is a company developing quantitative AI and quantum-inspired technologies to solve real-world problems across drug discovery, materials science, cybersecurity, and navigation. They build large quantitative models (LQMs) grounded in physics and chemistry to predict molecular properties, accelerate therapeutic design, and simulate complex chemical and catalytic processes, while also applying their expertise in AI and post-quantum cryptography to enhance digital security and navigation in GPS-denied environments.

📋 Description

• Implement, experiment with, and refine deep learning models for protein-ligand co-folding and structure prediction • Design and execute systematic evaluation pipelines to measure model performance against state-of-the-art methods and internal benchmarks • Collaborate with senior scientists and engineers to integrate validated models into production-ready drug discovery workflows • Employ computational and data analysis techniques to generate insights from structural and sequence datasets • Present research progress through internal scientific talks, technical write-ups, and contributions to peer-reviewed publications • Work closely with multidisciplinary teams to prototype and scale impactful solutions

🎯 Requirements

• Ph.D. in Computational Biology, Biophysics, Computer Science, Computational Chemistry, or a related field • Direct experience with protein structure prediction or protein-ligand co-folding methods (e.g., AlphaFold2/3, RoseTTAFold, Chai-1, Boltz, or comparable systems) • Experience developing, training, and validating deep learning models • Strong proficiency in Python and modern ML frameworks (PyTorch and/or JAX) • Demonstrated ability to design controlled experiments, interpret results critically, and iterate effectively on model development • Strong written and verbal communication skills; ability to work collaboratively in a fast-paced, multidisciplinary research environment • Active or recently completed postdoctoral research in co-folding, structure-based drug design, or closely related computational domain is highly desired. • Familiarity with binding affinity prediction methods • Authorship of publications or preprints in relevant venues (e.g., NeurIPS, ICML, Nature Methods, PLOS Computational Biology, bioRxiv). • Experience deploying ML workflows on public cloud infrastructure (e.g., GCP, AWS, or Azure) is a plus. • Exposure to generative models for protein/ligand design, active learning for data generation, foundation models for biomolecules, or QSAR/property prediction is a plus. • Familiarity with drug discovery workflows.

🏖️ Benefits

• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions • Retirement savings with company matching • Paid parental leave • Inclusive family-building benefits • Flexible paid time off • Company-wide seasonal breaks • Support for flexible work arrangements that enable sustainable performance • Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs