Computational Bioimaging Engineer

Role overview

We are looking for an engineer/scientist to lead the development of the computational imaging pipeline for our next-generation surgical endomicroscope. The successful candidate is able to work independently, and will take ownership of the computational imaging process, from raw detector signals to reconstructed images displayed in real time. This includes developing signal recovery and image reconstruction algorithms, optimizing their computational performance, and integrating them into our microscope hardware and acquisition software.

We are looking for someone who enjoys solving challenging technical problems, can make independent architectural decisions, and is comfortable taking a system from early development to a robust, working implementation.

Key responsibilities

• Design and implement the microscope's signal processing and image reconstruction pipeline.
• Develop algorithms for signal recovery, noise reduction, image reconstruction, and image quality improvement.
• Implement and optimize algorithms for real-time execution, including CPU and GPU acceleration where appropriate.
• Integrate reconstruction software with detector electronics, data acquisition hardware, and microscope control systems.
• Develop methods for calibration, synchronization, and correction of hardware-related signal artifacts.
• Evaluate and optimize imaging performance in terms of reconstruction quality, throughput, computational efficiency, and latency.
• Design reliable, modular, and maintainable software suitable for integration into a medical imaging system.
• Work closely with our team to translate hardware capabilities into computational imaging solutions.
• Take technical ownership of the computational imaging subsystem, from requirements and architecture to implementation, testing, and validation.

Required qualifications and experience

• MSc or PhD in electrical engineering, computer science, physics, computational imaging, or a related technical field, or equivalent industry experience.
• Strong background in digital signal processing, computational imaging, numerical methods, or image reconstruction.
• Excellent programming skills (Python, C++), with the ability to translate research algorithms into efficient, production-quality implementations.
• Experience with performance optimization, parallel processing, or GPU programming, preferably using CUDA.
• Experience working with raw measurement data from imaging systems, sensors, or scientific instruments.
• Ability to independently develop algorithms, evaluate alternative approaches, and make well-founded technical decisions.

Additional "nice to have" qualifications

• Demonstrated ability to take ownership of complex technical projects and deliver functioning systems with limited supervision.
• Experience with optical imaging systems, laser scanning microscopy, fluorescence microscopy, or nonlinear optical microscopy.
• Familiarity with low-photon-count imaging, or other low-SNR signal recovery techniques.
• Experience with real-time or low-latency processing pipelines, multithreaded programming, and streaming data.
• Experience integrating software with data acquisition hardware, FPGAs, or embedded systems.
• Knowledge of inverse problems, model-based reconstruction, computational optics, or machine learning for image restoration.
• Experience developing software for medical devices or other regulated systems.

Personal profile

We are particularly looking for someone who is technically curious, pragmatic, and comfortable working independently in an interdisciplinary environment. The ideal candidate combines a strong theoretical understanding of signal processing and imaging with a hands-on engineering mindset. They can identify the most promising technical approaches, rapidly prototype solutions, evaluate their performance experimentally, and turn successful prototypes into efficient and reliable software. Importantly, they should be comfortable taking full responsibility for their subsystem, proactively identifying challenges, and driving development forward in an unstructured and messy startup environment.

Interested or more questions? You can reach us at [email omitted] or send us a message!