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.