ML Research Engineer (AI for Life Sciences)

Role Overview

Join SandboxAQ’s AI Sim R&D team as a Machine Learning (ML) Research Engineer (AI for Life Sciences). You’ll help bring advanced research into production by architecting and optimizing scientific codebases powering ML and physics-based models for drug and materials discovery.

Responsibilities

• Architect, scale, and optimize scientific codebases for leading-edge ML and physics-based models.
• Drive prototype-to-product transition, turning high-impact research prototypes into robust products.
• Orchestrate distributed training pipelines on world-class GPU infrastructure.
• Lead ideation, benchmarking, and execution of complex datasets and ML models.
• Ensure models integrate seamlessly into large-scale simulation frameworks.

Profile / Requirements

• Research-to-software translation experience: ability to convert research papers into concrete ML software artifacts.
• Academic background: PhD or research-focused MSc in Computer Science, Physics, Chemistry, or a related quantitative field with advanced computational methods.
• Production software experience: Staff-level (5+ years) experience developing productionized software in professional teams.
• Life sciences/biopharma domain exposure: experience or training related to structural biology and/or affinity, structure-prediction, or generative chemistry models.
• Commercial product insight: track record of developing and launching successful commercial software.
• Advanced MLOps: familiarity with MLOps practices on major cloud platforms for automated scaling and model monitoring.
• Experience working in interdisciplinary environments where AI intersects with physical/biological sciences.

Nice to Have

• Direct experience developing models for biopharma use cases and structural biology data-science workflows.