[Hiring] Staff ML Research Scientist, Co-Folding and Affinity @SandboxAQ

Role Description

The AI Sim R&D team creates leading edge ML and physics-based models ("LQMs") to advance drug and materials discovery. We are a flexible, creative, and impact driven team of multidisciplinary scientists and engineers, whose products dramatically accelerate the creation of molecules and medicines.

As a Staff ML Research Scientist focusing on Co-Folding & Affinity, you will occupy a senior position architecting our ML biopharma capabilities. Your central purpose is to redefine the state-of-the-art in structure prediction and binding affinity, transforming these breakthroughs into core components of our software suite. Within your first year, you will:

• Pioneer novel deep learning architectures that surpass current benchmarks.
• Orchestrate the seamless integration of these models into production-ready drug discovery pipelines.
• Solidify SandboxAQ’s scientific authority through high-impact publications and industry-shaping research.

Qualifications

• PhD in Computer Science, Computational Chemistry, or a related field, with specific focus on structure-based deep learned affinity modelling a plus.
• At least 4 years of post-PhD experience, including experience in a professional industry setting, with a track record of delivering scientific impact that translates to product.
• Direct, hands-on experience developing and executing leading-edge co-folding and/or affinity prediction models, from proof of concept to productionized workflows.
• Proven excellence in co-folding and/or affinity prediction, as demonstrated by participation in industrial projects and/or academic publications.
• Experience functioning within a professional software team, including proficiency in Python and modern ML frameworks (PyTorch/JAX) at scale.

Requirements

• Postdoctoral experience in deep learned structure-based affinity models.
• Experience shipping commercial-grade software products within the biopharma or tech sectors.
• Relevant postdoctoral experience that demonstrates an ability to lead research at the intersection of AI and physical sciences.
• Direct experience working within drug discovery pipelines, understanding the specific challenges of lead optimization and hit-to-lead phases.
• Experience setting the technical roadmap for a specialized research group or project.
• A track record of contributions to the scientific community, such as first-author publications in top-tier venues like NeurIPS, ICML, or CVPR.
• Deep familiarity with agentic coding tools (e.g. Claude code, Codex).

Benefits

• Competitive base salary, performance-based incentives or bonuses (where applicable), and equity participation.
• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions.
• Retirement savings with company matching.
• Paid parental leave and inclusive family-building benefits.
• Flexible paid time off, company-wide seasonal breaks, and 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.