Staff ML Research Engineer, AI Simulation

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The Staff ML Research Engineer will bridge visionary research and production-grade reality, tasked with architecting, scaling, and optimizing scientific codebases that power large quantitative models for drug and materials discovery.

Responsibilities:

• Architect and Scale: Bring content of scientific papers into promising, scalable ML algorithms; and translate these into high-performing and robust scientific code
• ML Engineering: Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into our large-scale simulation frameworks
• GPU Expertise: Implement advanced software and hardware optimizations to maximize the efficiency of ML pipelines across distributed cloud GPU environments
• Ownership of the Lifecycle: Drive software through the entire product lifecycle—from foundational research and implementation to launch and long-term support—ensuring technical excellence at every stage

Requirements:

• MSc (PhD preferred) in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods
• Senior (5+ years) industry experience developing productionized software in professional teams
• Proven experience training and optimizing large-scale ML pipelines on distributed cloud GPUs (e.g. PyTorch, TensorFlow)
• Deep familiarity with agentic coding tools (e.g. Claude code, Codex)
• Experience supporting models in external-facing products, demonstrating the ability to bridge the gap between 'research code' and 'product code'
• Direct experience in biopharma or training leading-edge affinity, structure-prediction, or generative chemistry models
• A history of developing and launching successful commercial software products within a professional engineering team
• Familiarity with MLOps practices on major cloud platforms to support automated scaling and model monitoring
• Experience working in interdisciplinary environments where AI intersects with physical or biological sciences