Develops and leads innovative computational strategies that accelerate drug discovery across a Research and Development portfolio from early discovery through clinical candidate nomination.
Compensation
The annual base salary we reasonably expect to pay is $218,200.00-$298,000.00. Individual pay decisions depend on various factors, such as primary work location, complexity and responsibility of role, job duties/requirements, and relevant experience and skills. In addition, this position offers an annual bonus with a target of 35% of the earned base salary and eligibility to participate in our equity based long term incentive program.
Responsibilities
Develops, implements, and enforces scientifically sound computational chemistry strategies that support drug discovery programs from target validation through lead optimization and candidate selection
Leads the application of molecular modeling, structure‑based drug design, ligand‑based design, and AI/ML approaches to enable compound design and optimization
Partners closely with medicinal chemistry, structural biology, pharmacology, and data science teams to integrate computational insights into project strategies and decision‑making
Evaluates, interprets, and communicates computational results, proactively providing strategic direction to project teams and senior management on compound design and program progression
Drives innovation through the evaluation and implementation of emerging computational technologies, including generative chemistry, predictive modeling, and large‑scale data analysis
Requirements
Bachelor's in chemistry or related field
Master's in computational chemistry or related field
PhD in computational chemistry, organic chemistry or a related field
The role involves making the latest AI models run well on custom silicon, focusing on numerical optimization, LLM inference, and building agentic workflows for model performance.
Locations (4)
Sunnyvale, California, United States
Munich, Bavaria, Germany
North America
Europe
Responsibilities
Build agentic workflows to give agents tools to inspect models, change implementations and numerical settings, run emulation and hardware experiments, and diagnose failures.
Bring up new models quickly and take unfamiliar architectures from reference code to validated, performant execution on accelerators.
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