Responsibilities:

1) Build next-generation pathology AI models (core)

Build and deploy deep learning solutions for WSI analysis, including but not limited to object detection, image segmentation, and slide-level classification (e.g. prostate biopsy workflows).
Lead initiatives to enhance model accuracy, robustness, calibration process, and interpretability, including development of visual overlays, region-of-interest (ROI) summaries, and explainability tools to support clinical decision-making and quality assurance.
Develop and implement methods to address real-world variability in digital pathology, such as data drift and concept drift.

2) Own the full AI lifecycle (R&D to product)

Lead and manage end-to-end AI lifecycle, from R&D to productisation and clinical deployment.
Design, execute, and analyse experiments that translate research outcomes into production assets, including training pipelines, evaluation frameworks, ablation studies, benchmark comparison, error analysis, and comprehensive documentation.
Deveop and deliver production-ready model implementation, including model serving, performance monitoring, and continuous improvement workflow.
Collaborate closely with software engineering team to define and implement scalable deployment architectures (e.g. containerised services and APIs), optimize system performance, and ensure seamless integration into digital pathology workflows.

3) Clinical & regulatory-grade development

Work closely with pathologists and clinical collaborators to define clinical assessment strategies, adjudication workflows, and clinically meaningful performance metrics (e.g., concordance, sensitivity, discordance review triggers).
Contribute to validation planning and evidence generation consistent with clinical lab expectations (CAP/CLIA, etc).
Support quality system expectations for medical devices/IVDs.

4) Technical leadership

Promote and uphold industry best practices by leading code reviews, conducting technical assessments, and mentoring members of the AI team to ensure high standards of software quality and engineering excellence.
Design, define, and contribute to the product/tech roadmap by incorporating industry trends, emerging research, and state-of-the-art (SOTA) methodologies to drive innovation and long-term platform development.
Communicate clearly with multidisciplinary stakeholders (pathologists, product, regulatory, partnerships) on ideas, actionable and postmortem.
Demonstrate a strong understanding of the trade-offs across different AI use cases and apply a pragmatic, context-driven it depends mindset when making technical and strategic decisions.
Server as a subject matter expert of emerging technologies in computer vision and computational pathology by ensuring the tech stack remains at the forefront of the industry.

Requirements:

Minimum of 5 years of experience (or equivalent) in developing deep learning-based computer vision systems, with demonstrated ownership of at least one production-grade deployment.
Excellent proficiency in Python and PyTorch, supported by strong foundations in modern computer vision and machine learning methodologies.
Strong software engineering practices, including writing testable and maintainable code, participating in reviews, adhering to continuous integration standards, ensuring reproducibility, and implementing performance-optimised solutions.
Proficiency in Linux-based and containerized development decoupled with familiarity in model serving/monitoring in production.
Proven ability to design rigorous evaluation frameworks that reflect real-world clinical use cases.
Hands-on proficiency in leveraging cloud service provider(s) (AWS, GCP or Azure) to facilitate AI model deployments.

Preferred Qualifications

Minimum a Bachelor's degree (preferably Master's or PhD) in computer science, engineering or related discipline from a well-established and accredited academic institution.
Prior medical imaging / digital pathology experience (especially WSIs) and familiarity with pathology workflows.
Familiarity with computational pathology software (QuPath, CellProfiler, OpenSlide, etc.).
Track record of publications and/or impactful applied research integrated into products.
Demonstrated proficiency in MLOps (e.g., MLflow), including data-centric iteration, model drift monitoring, and proactive performance regression analysis.
Experience with domain generalization, stain normalization, multi-scanner robustness, and quality control tooling for WSI.
Familiarity with regulated product development constraints (risk management mindset, traceability, documentation), especially in medical device/IVD contexts.

Tech stack

Senior AI Engineer

AI/ML Senior Engineer