Staff Scientist, Algorithm Development

Spun out of Stanford and MIT, Stellaromics is an biotech startup founded in 2022 and located in Boston, MA. Our pioneering proprietary technology helps researchers and clinicians create comprehensive cellular maps enhancing our understanding of various diseases, with our flagship product STARmap and upcoming Pyxa™ suite of products. We have a passionate management team and committed investors who believe in our patented technology and overall mission to profoundly advance biomedical research and accelerate the discovery of life‑saving treatments.

We are seeking a highly skilled Staff Scientist, Algorithm Development to join our Computational Biology team. The ideal candidate will design, develop, and optimize computational methods to enhance 3D spatial transcriptomics analysis. In this role, you will leverage advanced statistical modeling and AI‑driven frameworks to improve data accuracy, scalability, and performance. Additionally, you will collaborate across teams to align technical solutions with research and product goals while effectively communicating complex insights.

Core Algorithm Development

• Lead the design, development, and validation of computational methods such as 3D image registration, spot detection, segmentation, decoding, and signal denoising to improve the accuracy and efficiency of spatial transcriptomics analysis.
• Develop advanced image and signal processing algorithms for object detection, feature extraction, spectral analysis, and noise reduction in complex multi‑dimensional biological imaging datasets.

Decoding & Data Integration

• Develop and optimize robust decoding algorithms to accurately assign transcripts from multiplexed, multi‑round imaging data.
• Design statistical models for error estimation, dropout detection, and confidence scoring to ensure decoding reliability.
• Explore methods such as sparse coding, matrix factorization, probabilistic inference, and embedding‑based machine learning to handle noise, optical crowding, and systematic artifacts.

Advanced Methods & Innovation

• Explore and apply cutting‑edge computational approaches, including deep learning (CNNs, VAEs, transformers) and generative AI models (e.g., denoising diffusion models) to improve both decoding and image analysis performance.
• Prototype novel algorithms for segmentation, denoising, decoding, and multi‑round signal integration in large‑scale 3D datasets.

Optimization & Scalability (Nice to have)

• Optimize image processing and decoding algorithms for parallel processing, GPU acceleration, and FPGA implementations to enable real‑time analysis of large datasets.
• Support integration of algorithms into scalable, high‑performance pipelines for both research and product deployment.
• Collaborate across scientific, computational, and product teams to align technical solutions with product requirements and research goals.
• Communicate complex concepts in decoding, image analysis, and signal processing to diverse audiences, including customers.
• Provide domain expertise in spatial transcriptomics, single‑cell analysis, image/signal processing, and statistical modeling to guide technical and strategic decisions.

• Ph.D. in Electrical Engineering, Computational Biology, Bioinformatics, Computer Science, or related field with a strong emphasis on image and/or signal processing.
• 5+ years of relevant experience (industry or postdoctoral research) in image processing, digital signal processing, computer vision, statistical modeling, or machine learning.
• Demonstrated expertise in segmentation, registration, feature extraction, and signal processing techniques.
• Strong background in computer vision, statistical modeling, probabilistic methods, and unsupervised clustering, especially applied to imaging and signal data.
• Excellent command of Python and/or C++ for scientific computing with experience in relevant libraries (OpenCV, scikit‑image, Num Py, etc.).
• Experience with deep learning frameworks (PyTorch, Tensor Flow), high‑performance computing, and large‑scale data pipelines.
• Proficiency in GPU computing; experience optimizing algorithms for GPU/parallel architectures is a strong plus.
• Experience…