Principal Computational Biologist, Computational Biology

About Us

SRT Therapeutics is a San Diego-based biotech company established in 2024 by the founders of Prometheus Biosciences, Steph Targan, MD (Cedars Sinai), Janine Bilsborough, Ph.D. (Cedars Sinai), the scientific team that discovered Tulisokibart (MK7240) and the role of TL1A in IBD as well as Scott Glenn and Lauren Otsuki, the drug development team that took Tulisokibart (MK7240) from academia into the clinic. Building on the demonstrated success of the precision medicine approach in Inflammatory Bowel Disease (IBD) with Tulisokibart, SRT Therapeutics will expand this approach by targeting key pathways that not only modulate inflammatory pathways, but also promote tissue healing in IBD.

Position Summary

We are seeking a creative and highly motivated Principal Computational Biologist to join our team as we develop the next generation of precision therapeutics for inflammatory diseases. As a Principal Computational Biologist you will serve as SRT Therapeutics' subject matter expert (SME) in machine learning and deep learning (ML/DL). Working with teams at the intersection of experimental and computational biology, you will guide our analytical strategies, driving precision medicine approaches to inflammatory disease drug development. Using a wide array of public and proprietary multimodal data, you will identify and validate disease biomarkers, drive patient segmentation strategies, and build virtual cell/tissue models for in silico drug screening and drug mechanism of action (MOA) studies.

Essential Duties and Responsibilities

• ML/DL Strategy & Leadership: Serve as the technical lead and SME for ML/DL initiatives; identify, evaluate, and implement state-of-the-art algorithms (e.g., Transformers, Graph Neural Networks, Multi-instance Learning) to solve fundamental problems in inflammatory diseases, from target identification to clinical response stratification.
• Multimodal Data Integration: Develop robust ML/DL models that fuse heterogeneous data modalities, combining high-dimensional omics data (scRNA-seq, spatial transcriptomics) with unstructured data (H&E pathology images, clinical notes/EHR).
• Strategic Evidence Generation: Guide our data acquisition strategy by integrating proprietary data assets with world-class cohorts (e.g., UK Biobank proteomics, Open Targets, and IBD-specific longitudinal consortia). Proactively identify, evaluate, analyze, and integrate mission-relevant genomic datasets into the company's knowledge base.
• Biomarker Discovery & Patient Stratification: Identify novel biomarkers for patient segmentation, pharmacodynamics, and response prediction in inflammatory disease indications.
• Virtual Modeling & Simulation: Lead the creation of "virtual cell" and tissue models to perform in silico drug screening and MOA studies to prioritize targets before wet-lab validation.
• Cross-Functional Collaboration: Partner closely with wet-lab biologists to design experiments that generate ML/DL-ready data and translate computational findings into actionable biological hypotheses.
• Mentorship & Communication: Mentor junior computational biologists in ML/DL best practices including scalable cloud computing and rigorous model validation.

Education and Experience

• Ph.D. in Computational Biology, Bioinformatics, Data Science, Computer Science, Statistics, or a related field with a focus on biological applications.
• 5+ years of post-graduate pharmaceutical/biotech industry experience applying ML/DL to biological datasets.
• Deep Learning Expertise: Demonstrated mastery of deep learning frameworks (PyTorch, TensorFlow, JAX) and architectures relevant to biology (e.g., VAEs, GANs, GNNs, Transformers).
• Multimodal Proficiency: Proven track record of utilizing ML/DL approaches to deliver actionable findings through the integration of two or more data modalities including both structured (*omics) and unstructured (imaging, electronic health records) data.
• Systems Biology: Deep understanding of systems-level data interpretation including gene networks and pathway analysis utilizing bulk and single-cell omics data.
• Programming: Expert-level fluency in Python and R; proficiency with cloud computing environments (AWS), containerization (Docker), and version control (git).
• Computer Vision (Preferred): Experience with computational pathology, digital image analysis, or computer vision techniques applied to biological imaging including QC, segmentation, CNNs, transformers.

Benefits

Competitive compensation package including healthcare coverage, FSA, voluntary life insurance, 401(k) retirement plan, holidays, PTO, performance-based bonus opportunities, and equity participation.

Pay Range: $160,000 - $215,000