Staff ML Research Engineer, SandboxAQ

• Academic Foundation: MSc (PhD preferred) in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods
• Software Excellence: Senior (5+ years) industry experience developing productionized software in professional teams
• Distributed Systems: Proven experience training and optimizing large-scale ML pipelines on distributed cloud GPUs (e.g. PyTorch, TensorFlow)
• Agentic Coding: Deep familiarity with agentic coding tools (e.g. Claude code, Codex)
• 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 Engineer, you will be the bridge between visionary research and production-grade reality, tasked with making in silico design the dominant paradigm in drug discovery
• Your central purpose is to architect, scale, and optimize the scientific codebases that power our LQMs. Over your first year, you will drive the transition of high-impact prototypes into robust products, orchestrate distributed training pipelines on world-class GPU infrastructure, and pioneer hardware-level optimizations that push the boundaries of computational chemistry
• 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