ML Research Engineer, SandboxAQ

• Academic Foundation: PhD, or research-focused MSc, in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods
• Software Excellence: Staff (5+ years) industry experience developing productionized software in professional teams
• Structural Biology Exposure: Experience or training in data-science related tasks related to structural biology
• Research Translation: Expertise translating research papers into concrete ML software artifacts
• Product Lifecycle Mastery: Experience supporting models in external-facing products, demonstrating the ability to bridge the gap between "research code" and "product code"

Desirable

• 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 SandboxAQ’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 SandboxAQ’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 SandboxAQ 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