Sr/Staff Engineer, Computational Biology & Imaging

Countable Labs•Palo Alto, CA

14h

About The Position

At Countable Labs (formerly Enumerix), we’re reimagining the future of genomics—and we’d love for you to be a part of it! As the innovators behind our groundbreaking Countable PCR platform, we’re building tools that make a real impact in precision medicine. We’re a fast-growing startup fueled by innovation, collaboration, and a mission-driven spirit. If you’re ready to roll up your sleeves, build something from the ground up, and help shape the future of genomics, we want you on our team! Role overview We’re seeking a Sr/ Staff Engineer, Computational Biology & Imaging to develop the backend algorithms and computational infrastructure behind our imaging analysis platform. This is a deeply technical role centered on algorithm development, scientific analysis, computational optimization, and product-grade software design. You will design and implement core analysis modules, optimize algorithms for performance and robustness, architect reliable and maintainable pipelines, and work closely with R&D scientists to support data exploration and troubleshooting. This role is ideal for an engineer who thrives at the intersection of imaging, algorithm design, and biological data interpretation, and who values building software that is robust, accurate, and production-ready.

Requirements

PhD (or equivalent experience) in Computational Biology, Bioinformatics, Computer Science, Applied Physics, or a related quantitative field.
3+ years of industry experience building customer-facing imaging or computational analysis tools.
Hands-on experience working with large and complex datasets, including techniques such as statistical analysis, dimensionality reduction, denoising and noise modeling, normalization, and clustering.
Proficiency in Python and MATLAB; experience with C++ or C# is a plus.
Strong software engineering fundamentals, including testing, documentation, and version control.
Thrives in a fast-paced, interdisciplinary startup environment working closely with cross-functional teams.
Excellent communication and organizational skills, with the ability to work independently and drive technical work forward.

Nice To Haves

Experience applying deep learning frameworks (PyTorch, TensorFlow) to imaging or quantitative data.
Familiarity with fluorescence imaging, microscopy, or other imaging modalities.
Exposure to spatial or single-cell genomics or other high-dimensional biological datasets.
Experience developing visualization tools for scientific or analytical workflows (Plotly, Dash, Bokeh, PyQt).
Experience with workflow automation, data versioning, or pipeline orchestration systems.
Experience with GPU acceleration, vectorization, or parallelization for scientific computing.

Responsibilities

Algorithm Development Develop, implement, and refine algorithms for processing, analyzing, and interpreting large 3D imaging datasets at scale.
Build high-performance MATLAB and Python code optimized for speed, memory efficiency, maintainability, and scalability.
Improve algorithmic robustness across diverse imaging datasets and analytical workflows.
Scientific Data Analysis & Cross-Functional Problem Solving Work closely with scientists to analyze experimental datasets, interpret complex imaging results, and identify sources of variability.
Perform exploratory and ad hoc data analysis to support assay development, validation, and troubleshooting.
Investigate anomalies or unexpected results, determine root causes, and recommend data-driven algorithmic or workflow improvements.
Develop QC metrics, visualizations, and diagnostic tools that enable clear interpretation of experiment outcomes.
Translate scientific observations into computational approaches and communicate analytical findings and technical considerations across interdisciplinary teams.
Production-Grade Software Engineering Write clean, well-structured, well-tested analytical software suitable for integration into production systems.
Develop unit tests, regression tests, and automated workflows to ensure reliability and reproducibility.
Collaborate with software engineers to integrate computational modules into the main application.
Contribute to internal infrastructure that supports scalable and maintainable analysis pipelines