About the Role:

We are on a quest to find a pioneering engineer to join our Computational Biology group, a team at the forefront of developing single-cell and spatial genomic assays. This role is crucial for advancing our in-situ platforms and assays, which have revolutionized how researchers explore complex biological systems across various fields such as cancer biology, immunology, and neuroscience. The ideal candidate will possess a deep understanding of statistical modeling, numerical optimization algorithms, and the practical application of these skills, striking the right balance between classical and modern approaches. You should be a self-starter, capable of independent thinking, and thrive in a highly collaborative, interdisciplinary, and fast-paced environment. You have experience working on large code repositories.

What You Will Be Doing:

• Implementing signal processing, pattern matching, and model fitting pipelines to analyze large multidimensional and multimodal datasets to improve our spatial and in-situ assays.
• Creating analytical models or simulations of the data generating process to elucidate and communicate the trade-offs involved in system design, data quality and algorithms.
• Optimizing algorithms leveraging CPU/GPU architecture to enhance data processing capabilities.

To Be Successful in This Role, You Will Need:

• PhD, MS, or equivalent industry experience in a quantitative field such as computational biology, computer science, physics, mathematics, statistics, or electrical engineering.
• Proficient programming skills in Python and compiled languages (e.g. C/C++, Rust, etc), with a solid background in deploying code on GPUs.
• The ability to research, adapt, and innovate upon existing methods from literature and libraries, transforming them into well-architected, efficient code.

Additional Desirable Skills to Have:

• Exposure to multiplexed imaging and sequencing technologies, and spatial biology.
• A creative problem-solving approach that prioritizes extracting maximum performance from code, while navigating the imperfections inherent in real-world data and maintaining theoretical integrity.
• Knowledge of sequence alignment models, information theory, inverse problems.

This role is tailored for an individual who is not only technically proficient but also possesses a strategic mindset, capable of driving projects forward in a dynamic research and development landscape. If you are passionate about leveraging computational power to unlock the mysteries of biology at an unprecedented scale, we encourage you to apply.