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

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

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

Company:

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 Staff AI 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 clinical-grade implementation.

This is not a remote position - this role is fully on site.

Key Responsibilities:

• AI Architecture for Genomics: Lead the design and deployment of AI/ML frameworks optimized for high-dimensional cf-RNA sequencing data to deliver clinically actionable AD insights.
• Biological Data Engineering: Develop sophisticated "AI-ready" data preprocessing workflows, including advanced methods for differential expression, batch effect mitigation, and normalization that preserve subtle biological signals.
• Model Innovation: Build and fine-tune predictive models, ranging from traditional ensembles (Survival models, Random Forest, XGBoost) to Deep Learning architectures and RNA-seq Foundation Models (LLMs) for feature extraction and zero-shot inference.
• Translational Validation: Bridge the gap between "silicon" performance and "clinical" reality by leading hypothesis-driven investigations to ensure AI outputs meet rigorous regulatory and biological standards.
• Scalable AI Pipelines: Architect end-to-end pipelines that integrate raw NGS outputs into scalable cloud-based AI feature stores, ensuring reproducibility and data governance.
• Strategic Leadership: Collaborate with wet-lab and clinical teams to translate complex AI narratives into strategic roadmaps for executive leadership.

Qualifications:

• PhD in a Quantitative Biological Field: (e.g., Computational Biology, Bioinformatics, Applied Statistics, or Biophysics). We are looking for an "AI Scientist" who speaks the language of biology.
• 1+ Years in Biotech/Diagnostic Industry: Proven track record of taking complex NGS-based models from R&D to a validated state.
• Deep NGS Domain Expertise: Mastery of the cfRNA/mRNA-seq workflow. You must understand library prep artifacts, noise quantification, and dimensionality reduction as the "preprocessing layer" for AI.
• Advanced ML/DL Toolkit: Extensive experience with PyTorch, TensorFlow, or JAX, specifically applied to genomic datasets. Experience with multi-modal AI (integrating clinical and molecular data) is a major plus.
• Algorithmic Creativity: Ability to move beyond "off-the-shelf" libraries to develop custom loss functions or architectural tweaks that account for the unique distribution of RNA-seq count data.
• Technical Stack: Proficient in Python (the lingua franca of AI) and Linux; experience with AWS (Sagemaker, EC2) and modern software practices (CI/CD, Git).
• Scientific Mindset: A first-principles thinker who prioritizes model interpretability and biological "truth" over black-box performance metrics.

Are you ready to take the next step in your career as a Staff Data Scientist at Superfluid? We’d love to hear why you’re the perfect fit for our team! Send us your resume along with a brief note on what makes you a great candidate to careers@superfluiddx.com.

Pay: $160,000.00 - $200,000.00 per year

Benefits:

• 401(k)
• Dental insurance
• Employee assistance program
• Health insurance
• Life insurance
• Paid time off
• Vision insurance

People with a criminal record are encouraged to apply

Work Location: In person