• 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

Benefits

• Stock options
• Unlimated vacation (PTO) and flexible work culture
• Company-wide winter and summer breaks to unwind
• Best-in-class health plans: Medical, dental and orthodontics, and vision
• Family planning and fertility benefits
• Military leave
• 100% paid parental leave
• 401k with company matching
• Education stipends
• Financial wellness resources- Agentic Coding: Deep familiarity with agentic coding tools (e.g. Claude code, Codex)
• Distributed Systems: Proven experience training and optimizing large-scale ML pipelines on distributed cloud GPUs (e.g. PyTorch, TensorFlow)
• Software Excellence: Senior (5+ years) industry experience developing productionized software in professional teams
• Academic Foundation: MSc (PhD preferred) in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods
• Product Lifecycle Mastery: Experience supporting models in external-facing products, demonstrating the ability to bridge the gap between “research code” and “product code”
• Domain Expertise: Direct experience in biopharma or training leading-edge affinity, structure-prediction, or generative chemistry models
• Commercial Insight: A history of developing and launching successful commercial software products within a professional engineering team
• Advanced MLOps: Familiarity with MLOps practices on major cloud platforms to support automated scaling and model monitoring
• Collaborative Innovation: Experience working in interdisciplinary environments where AI intersects with physical or biological sciences