Stellaromics Inc. Machine Learning Scientist - Spatial Transcriptomics Boston, MA · Full time

We are seeking a highly skilled and motivated Machine Learning Scientist to join our dynamic team, focusing on Spatial Transcriptomics using Stellaromics' cutting-edge STARmap and RIBOMap technologies. The ideal candidate will be proficient in advanced machine learning and deep learning techniques, with a strong background in image analysis, high-performance computing, and a keen interest in the intersection of computational science and biology.


Key Responsibilities:

  • Develop and implement advanced machine learning and deep learning models, including but not limited to Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformers for the analysis of spatial transcriptomics data.
  • Handle and analyze large datasets with proficiency in image segmentation, classification problems, and visualization techniques for high dimensional datasets relating to spatial transcriptomics data.
  • Leverage unsupervised clustering techniques to analyze and interpret complex datasets, facilitating the discovery of new biological insights.
  • Conduct spatial modeling and analysis to derive meaningful insights from large-scale spatial transcriptomics datasets.
  • Efficiently and consistently deliver end-to-end projects, from conception through to deployment, within set deadlines.
  • Proactively manage project timelines and deliverables, ensuring that milestones are met, and products are delivered to the highest quality standards.
  • Engage in high-performance computing tasks to process and analyze large-scale spatial transcriptomics datasets efficiently.
  • Execute service projects for customers, ensuring high-quality results and satisfaction.
  • Ensure rigorous data management and documentation practices, maintaining high standards of quality and reproducibility.
  • Collaborate effectively within a team-based environment, contributing to a positive and productive work culture.
  • Communicate complex concepts and findings clearly and effectively, both verbally and in writing, to a variety of audiences, including customers.


  • Master’s or Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a related field.
  • Strong preference for candidates with practical experience in product development and a history of meeting project deadlines.
  • Demonstrated expertise in machine learning and deep learning, with proficiency in Python/C/C++, PyTorch/ Tensorflow, and advanced algorithms such as GNNs, Transformers, and CNNs.
  • Experience with high-performance computing environments, and familiarity with executing computational tasks on large-scale datasets.
  • Proficiency in probabilistic modeling, unsupervised clustering, and visualization of high dimensional datasets.
  • Knowledge of spatial modeling and analysis is a strong advantage.
  • Excellent verbal and written communication skills, with the ability to document and manage data efficiently.
  • Strong work ethic, with a proven ability to work effectively in a team-oriented environment and handle customer-facing responsibilities with professionalism.
  • Strong background in spatial transcriptomics /proteomics, and biological image analysis is highly preferred.
  • Submission of a GitHub profile link showcasing previous projects or contributions is encouraged.