ML Research Scientist, Co-Folding and Affinity

About SandboxAQ

Sandbox

AQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.

We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.

At Sandbox

AQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.

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, and solidify Sandbox

AQ’s scientific authority through high-impact publications and industry-shaping research.

• Pioneer Novel Architectures: Drive the research and development of next-generation deep learning models for protein-ligand co-folding and affinity prediction.

• Architect Product Integration: Bridge from research to commercial utility by equipping Sandbox
  
  AQ’s software products with advanced predictive capabilities.

• Orchestrate Technical Strategy: Bring novel ideas and the content of scientific papers into the ideation, training, and benchmarking of complex models, ensuring they are optimized for large-scale, real-world drug discovery applications.

• Champion Scientific Excellence: Act as a technical beacon for the team, representing Sandbox
  
  AQ scientifically and shaping its vision externally and internally.

• Scale High-Performing Teams: Mentor junior researchers and collaborate across engineering and product teams to foster a culture of technical rigor and rapid iteration.

• World-Class Domain Expertise: PhD in Computer Science, Computational Chemistry, or a related field, with specific focus on structure-based deep learned affinity modelling a plus.

• Proven Industrial Impact: 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.

• Frontier Technical
  
  Skills:
  
  Direct, hands-on experience developing and executing leading-edge co-folding and/or affinity prediction models, from proof of concept to productionized workflows.

• Domain-Specific Excellence: Proven excellence in co-folding and/or affinity prediction, as demonstrated by participation in industrial projects and/or academic publications.

• Professional Engineering Fluency: Experience functioning within a professional software team, including proficiency in Python and modern ML frameworks (PyTorch/JAX) at scale.

• Postdoctoral Experience
  :
  In deep learned structure-based affinity models.

• Commercial Success: Experience shipping commercial-grade software products within the biopharma or tech sectors.

• Interdisciplinary Leadership: Relevant postdoctoral experience that demonstrates an ability to lead research at the intersection of AI and…