As a Fullstack Software Engineer, you will own and evolve the platform used by computational chemists, build tools for molecular visualization, and scale data infrastructure for drug discovery.
Supports commercial assessments for AI-enabled drug pipeline programs by analyzing markets and competitors, informing product and clinical development strategy, and aligning cross-functional teams.
Apply and improve machine learning models in active drug discovery programs, validating predictions, guiding experimental teams on model quality and uncertainty, and connecting research with practical discovery needs.
Build and improve workflow orchestration systems and large-scale scientific data pipelines, optimizing computation, caching, and distributed execution to make molecular AI workflows faster and more reproducible.
Own the roadmap and execution of internal AI and computational research platforms, connecting machine learning, engineering, chemistry, biology, and lab operations to improve drug discovery workflows and accelerate model–lab feedback.
As an ML Research Engineer, you will drive the development and scaling of foundation models for molecular science, collaborating closely with researchers and scientists to translate innovative ideas into practical applications.
Lead research on generative and predictive foundation models for molecular systems, developing novel algorithms and large-scale experiments that advance drug discovery and contribute to the scientific community.
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