Revvity's In Vivo Imaging group does incredibly important work supporting research across disease models for cancer, cardiopulmonary, metabolic, and infectious disease. The team is an interdisciplinary group that develops market-leading preclinical instrumentation, software, and reagents that drive innovation. In this role as Senior Machine Learning Engineer, your primary focus will be on multi-modal image analysis software, one of the newest entrants to Revvity's product portfolio.

Senior Machine Learning Engineers in our group focus primarily on solving problems related to image segmentation, classification, and registration. These individuals have a substantial background in medical imaging physics, digital signal processing, estimation, deep learning, and/or mathematical modeling. They are strong programmers, including experience in high level scripting languages (e.g., Python/MATLAB) for rapid prototyping and low-level languages (e.g., C, C++, CUDA) for algorithm optimization. They are also comfortable working independently, diving into literature, and running hypothesis-driven experiments when solutions are not obvious.

While hands-on experience in system-level design of imaging hardware is not required, it is preferred. Likewise, hands-on preclinical study design and execution experience is not required but also preferred. Without these prior experiences, candidates should have an interest in getting acquainted both with the instrumentation and experimental laboratory workflows to understand the needs of our customers and provide excellent user experiences. They have a can-do attitude, and willingness to work hard to achieve our common goals.

This job is based in Research Triangle Park, North Carolina.

Required Qualifications:

• Ph.D. or Master's degree in Biomedical Engineering, Computer Science, or related field.

• Minimum 3-6 years' experience as a machine learning engineer, data scientist, or equivalent.

• Minimum 4 years' experience programming in scientific scripting languages (Python, MATLAB).

• Minimum 2 years' experience developing and training models using common frameworks (PyTorch, TensorFlow, ONNX).

• Minimum 2 years' experience programming in C/C++/CUDA.

Preferred Qualifications:

• Experience with automated medical image segmentation and classification.

• Experience designing and implementing ML solutions in the cloud or in hybrid architecture (AWS).

• Experience programming with open-source medical image processing, visualization, and deep learning toolkits (e.g. MONAI, 3D Slicer, VTK, ITK, etc.).

• Experience in the development of graphical user interfaces (GUIs), preferably cross-platform frameworks (e.g. Qt).

• Familiarity with model registry and tracking using MLFlow or similar.

• Experience working in a team-oriented, collaborative environment.

• Additional personal attributes:

  • Demonstrated ability to work independently and research innovative solutions to challenging technical problems.

  • Desire to work in a dynamic and fast-paced entrepreneurial environment.

  • Comfortable presenting and demonstrating results in live settings in front of stakeholders and/or customers.

  • Strong technical writing abilities and analytical skills.

  • Detail oriented, focused on facts and objectives.

  • Persistent and structured but flexible when challenged with competing priorities.

  • Strong interpersonal and relationship-building skills.