Bring content of scientific papers into promising, scalable ML algorithms; and translate these into high-performing and robust scientific code
Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into our large-scale simulation frameworks
Implement advanced software and hardware optimizations to maximize the efficiency of ML pipelines across distributed cloud GPU environments
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 (highly desired)
A history of developing and launching successful commercial software products within a professional engineering team (highly desired)
Familiarity with MLOps practices on major cloud platforms to support automated scaling and model monitoring (highly desired)
Experience working in interdisciplinary environments where AI intersects with physical or biological sciences (highly desired)
Tech Stack
Cloud
PyTorch
Tensorflow
Benefits
Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
Retirement savings with company matching
Paid parental leave and inclusive family-building benefits
Flexible paid time off
Company-wide seasonal breaks
Support for flexible work arrangements that enable sustainable performance
Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs