Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Lead the development of a differentiable CFD framework for optimizing chemical reactor designs, involving scalable GPU engineering and numerical optimization. Collaborates with AI developers to validate and document simulations.

Lead customer onboarding and implementation of AI Simulation solutions, establish reference architectures, support technical escalations, and drive enterprise deployment best practices.

The Senior Backend Engineer will develop core functionalities for AI-driven drug discovery, collaborating with R&D and product teams, managing infrastructure, and ensuring engineering excellence.

As a Staff ML Research Engineer, you will develop and optimize ML algorithms for drug discovery, transitioning high-impact prototypes into robust products and managing software development across the lifecycle.

Develop and implement state estimation and localization algorithms for novel navigation solutions, conduct R&D on navigation techniques, and test their performance.

The Staff ML Research Scientist will lead research in deep learning for drug discovery, develop innovative models, and integrate them into commercial software. Responsibilities include mentoring junior researchers and contributing to scientific publications.

Lead risk modeling initiatives from data exploration to deployment, defining methodologies, advising clients, and integrating models into platforms.

Lead the design and management of global compensation and benefits programs while collaborating with finance and HR to attract and retain talent.

The Director of Product Management for AI Generation Engine will lead product strategy and execution in Finance & Risk Modeling, focusing on innovative AI solutions and close collaboration with financial services clients.

Lead high-touch onboarding and implementation of AISim solutions, design reference architectures and integration patterns, enable secure enterprise deployments, provide technical escalation and root-cause analysis, and feed product and engineering to improve operability and customer experience.