Overview
In this role you will drive intelligent image analysis within our scientific imaging products. You will act as the ML technical expert, researching, developing and deploying advanced models to enhance image analysis, processing, object detection and segmentation. You’ll integrate ML into high-performance desktop applications and shape the C++ architecture with a focus on scalable, production-ready software. You’ll work at the intersection of software, ML and scientific imaging on large multidimensional datasets, contributing to innovations that advance science. This is a senior, impact-driven opportunity in a globally renowned imaging leader with a strong focus on collaboration and technical
Pay / Benefits
- Private Medical Insurance
- Employee Assistance Programme
- Mental Health First Aiders
- Income Protection
- Life Assurance
- 6% employer pension contribution
Responsibilities
- Research, develop and deploy ML solutions for image analysis, processing, object detection and segmentation
- Evaluate ML approaches and options, including custom models or state-of-the-art solutions
- Design, develop and maintain robust software using modern engineering practices
- Integrate ML algorithms into high-performance desktop applications and contribute to C++ architecture
- Profile and optimize models for CPU/GPU, handling large 3D/4D datasets
- Provide technical guidance, mentoring and support to engineers and test teams
- Collaborate with software engineers, scientists, product managers and domain experts, and travel to other sites when required
Key requirements
- MSc in a quantitative field or equivalent industry experience
- Senior-level software engineering and ML development experience
- Track record delivering ML projects from concept to production
- Strong OO design and hands-on programming
- Experience researching, evaluating and implementing ML solutions for real-world applications
- Deep understanding of ML theory and image processing fundamentals
- Proficiency with PyTorch and/or TensorFlow, and Python-based ecosystems
- Ability to independently research and implement innovative solutions
- collaboration
- curiosity and learning
- ownership and accountability
- CNNs and U-Net architectures
- PyTorch and/or TensorFlow
- Python scientific stack
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