Superfluid DX, Inc.

AI Data Scientist (Transcriptomics & cfRNA for Alzheimer’s Disease Research)

South San Francisco, CA, US$160,000-$200,000Posted 5 days ago

Job Description

Role: AI Data Scientist (Transcriptomics & cfRNA for Alzheimer’s Disease Research)

Location

On Site 249 East Grand Avenue, South San Francisco, CA 94080

Company

Superfluid DX, Inc.

About Us

Superfluid is developing the first high-performance, predictive blood-based test for Alzheimer’s Disease (AD), related dementias and cognitive decline that directly assays mRNA transcripts from the brain via its platform technology of cell-free messenger RNA (cf-mRNA) analysis. This next-generation novel liquid biopsy technology enables non-invasive measurement of the dynamic biology of organs throughout the body, including the brain. Our precise understanding of the underlying pathways of disease will transform AD disease care and treatment.

Superfluid has a small but mighty team including Founder Steve Quake (Stanford Professor and Head of Science at CZI) and CEO Gajus Worthington (Former Founder/CEO of Fluidigm). Superfluid has published extensively in multiple peer-reviewed journals. Superfluid is well funded by notable investors including Brook Byers and Reid Hoffman and is also supported by the NIH and Alzheimer's Drug Discovery Foundation.

Position Overview

We are seeking a visionary AI Data Scientist to join our cell-free RNA (cf-RNA) team. This role is designed for a technical leader who can bridge the gap between advanced Generative AI/Deep Learning and the intricate biology of cf-RNA. You will drive the development of next-generation predictive and prognostic models for AD by architecting AI solutions that are fundamentally grounded in biological rigor.

The ideal candidate understands that "Garbage In, AI Out" is the primary risk in liquid biopsy. We need a scientist who can master the secondary and tertiary analysis of cf-RNA (normalization, batch correction, noise quantification) to build AI models - including, but not limited to, foundation models and Transformer-based architectures - that are robust enough for clinica...

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