Principal Computational Biologist, Computational Biology

Job Title: Principal Computational Biologist, Computational Biology Company: SRT Therapeutics Location: San Diego, CA. On-site. Reports to: Director of Computational Biology, R&D Pay Range: $160,000-215,000 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. This strategy will move SRT Therapeutics beyond the current empirical treatment model to realize the goal of enhancing both outcomes and the quality of care for our patients. 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. These activities are critical in our mission to bring safe and effective precision medicines to patients. 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. C ontribute to evidence generation strategies across clinical trials, precompetitive consortia, and population biobanks.
Biomarker Discovery & Patient Stratification: Identify novel biomarkers for patient segmentation, pharmacodynamics, and response prediction in inflammatory disease indications. Partner with functional biomarker leads across precision medicine and pipeline programs to prioritize target engagement and translational biomarker strategies.
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. Serve as a technical lead, translating complex findings into actionable insights and presenting results to cross-functional teams and leadership. Contribute to a collaborative culture. 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, including translating computational findings into actionable biological and therapeutic insights.
Deep Learning Expertise: Demonstrated mastery of deep learning frameworks (PyTorch, TensorFlow, JAX) and architectures relevant to biology (e.g., VAEs, GANs, GNNs, Transformers). Extensive experience evaluating and implementing ML/DL approaches designed to handle the noise, sparsity, and high-dimensionality inherent in incomplete human clinical and omics datasets.
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: We provide a competitive compensation package designed to reward performance and support employee wellbeing. Our benefits include healthcare coverage, flexible spending accounts (FSA), voluntary life insurance, a 401(k) retirement plan, holidays, paid time off, performance-based bonus opportunities, and equity participation so employees can share in the company’s long-term growth and success.