ML Research Engineer, Foundation Models (Senior / Staff / Principal)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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About this role

ML Research Engineer, Foundation Models (Senior / Staff / Principal) San Mateo, CA, New York, NY ML Research Engineer, Foundation Models About the Team Join a world-class team at the forefront of AI and biochemistry. At Genesis Molecular AI , we're a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that unlock new therapies for patients with severe diseases . We conduct fundamental research at the intersection of machine learning, physics, and computational chemistry , pushing the boundaries of each field. The Genesis AI team is building the engine for this revolution. We develop large-scale generative models trained across the full spectrum of molecular data, supported by extensive compute infrastructure and simulation pipelines. The work sits at the intersection of machine learning research, structural biology, and computational chemistry , requiring deep technical rigor and strong interdisciplinary collaboration. About the Role This role is for a highly skilled ML Research Engineer who thrives at the intersection of fundamental research and production-grade engineering . As a core member of the Genesis AI team, you will serve as the engineering pillar for inventing, scaling, and shipping our next generation of foundation models for molecular science . You will partner closely with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge model ideas into systems that power real drug discovery programs . Your work may involve: - Scaling model pretraining

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