Principal Computational Scientist

Position Summary: We are seeking a Principal Computational Scientist to help lead the development of the virtual tissue model as Singleton’s core discovery platform. As the company’s first computational hire, you will take ownership of the model, training data, in silico perturbation screens, and analysis pipeline to support target identification, next-generation model architecture, and downstream drug development applications. You will work closely with all members of the Singleton team including the CTO, who leads model architecture; our Head of Computational Biology; and the pharmacology team. This role suits a scientist who has trained deep learning models on single-cell or spatial data and has also taken responsibility for how those models are evaluated and used.

Key Responsibilities: Own the CIFM codebase and trained models on Singleton infrastructure, including training, inference, checkpointing, and versioning Retrain and adapt the model on Singleton’s growing spatial transcriptomics corpus, and assess where additional data or model capacity improves performance Design and run the benchmarks used to evaluate model performance, including baseline comparisons, patient- and section-level held-out splits, and out-of-distribution tests across tissues and assay platforms Characterize model limitations and uncertainty, and communicate them clearly to internal teams and external partners Run in silico perturbation screens to nominate targets and combinations for our disease programs Work with pharmacology team on the model analysis strategy to yield mechanistic hypotheses and inform cell/tissue-based validation experimental approaches; use experimental results to improve the model Present analyses to leadership, partners, and investors clearly and rigorously Contribute to scientific strategy, publications, and patents Help hire and mentor future members of the computational team QualificationsRequired Ph.D.in computational biology, machine learning, bioinformatics, or a related quantitative field, with 8+ years of relevant industry or applied research experience (exceptional candidates with fewer years will be considered) Demonstrated experience designing and training deep learning models on biological data in PyTorch, including multi-GPU or distributed training Deep hands-on experience with single-cell and spatial transcriptomics data, including quality control, normalization, batch correction, and integration across platforms Track record of taking research code to reproducible, maintainable software used by others Strong software engineering fundamentals: Python, Git, Linux, containerized environments, and cloud computing (GCP or AWS) Ability to critically evaluate model performance and analytical choices, including rigorous benchmarking against appropriate baselines Demonstrated ability to independently lead scientific initiatives and collaborate across computational and experimental teams Excellent written and verbal communication skills, including presenting complex results to non-specialist audiences Preferred Experience with graph neural networks or geometric deep learning Experience with self-supervised, masked, or generative modeling approaches Experience with perturbation modeling or perturbational genomics (e.g., Perturb-seq, CRISPR screens, in silico perturbation) Experience with imaging-based spatial platforms (e.g., 10x Xenium, NanoString CosMx, Vizgen MERSCOPE) and their technical limitations Experience with structure-guided protein design (RFdiffusion, ProteinMPNN, AlphaFold/Boltz, RosettaFold, etc.) and familiarity with molecular dynamics simulation tools Knowledge of immunology, tissue microenvironment biology, or ligand–receptor signaling Drug discovery experience, including translating computational predictions into experimental validation Publications in computational biology or machine learning venues Experience managing cloud compute resources and budgets for large-scale training