Imaging ML Scientist - Pathogen Diagnostics (On-Site)

PicturaBio; Packmoor; On-site; GBP 40,000 - 60,000; Full time

Job summary

A biotechnology company in Packmoor is seeking a Machine Learning Scientist to develop and maintain imaging-based classification models for rapid pathogen detection. The successful candidate will work closely with assay scientists and engineers, applying rigorous evaluation practices. A background in image-based data analysis and strong Python skills are essential. Candidates will thrive in a dynamic start-up culture focused on innovative solutions. This is an on-site role, ensuring close collaboration with laboratory teams.

Qualifications

• Strong experience with image-based datasets, especially fluorescence microscopy images.
• Proven experience in developing image segmentation and classification pipelines.
• Experience working with laboratory-generated or experimental data is an advantage.

Responsibilities

• Develop and maintain algorithms for pathogen classification in microscopy images.
• Train and evaluate machine learning models for biological particles.
• Work closely with lab scientists to interpret imaging data.

Skills

Strong Python skills

Scientific computing (NumPy, Pandas, SciPy)

Image processing and computer vision (e.g. OpenCV)

Deep learning for images (e.g. CNNs)

Version control and collaborative development (Git)

Clear communication skills

Education

Bachelor’s or Master’s degree in relevant field

Tools

PyTorch

TensorFlow

scikit-learn

Job description

A biotechnology company in Packmoor is seeking a Machine Learning Scientist to develop and maintain imaging-based classification models for rapid pathogen detection. The successful candidate will work closely with assay scientists and engineers, applying rigorous evaluation practices. A background in image-based data analysis and strong Python skills are essential. Candidates will thrive in a dynamic start-up culture focused on innovative solutions. This is an on-site role, ensuring close collaboration with laboratory teams.