About the job
As a Staff Machine Learning Engineer, you will be responsible for driving the design, development, and deployment of novel machine learning solutions for pathology image analysis. You will work with alongside research, engineering, regulatory, and clinical teams to develop and test algorithms and translate them into robust, validated, and scalable medical device software. The ideal candidate will have a proven track record of leading efforts to build and deploy cutting-edge deep learning models on large-scale image data. Ability to work in person in San Carlos, CA office is preferred.
Responsibilities
Independently lead projects to conceive, develop, and implement AI/ML approaches to extract novel insights from large-scale microscopy image data sets
Perform analysis of neural networks, propose and execute experiments to improve key performance metrics
Update and improve primary machine learning models as more data is generated
Support software infrastructure and data engineering required to store, annotate, train, and test on large pathology image sets
Implement semi-supervised and self-supervised methods to reduce image annotation burden
Collaborate with physicians and product teams to ensure clinical relevance, robustness, and usability of models
Stay up to date with the latest pathology CV/ML literature, use this to inform research & product direction
Optimize and validate models for integration into production systems, ensuring performance in real-world clinical settings
Develop applications to be deployed and scaled
Qualifications
Minimum
PhD or Master’s degree in Computer Science or related field with a focus on ML/CV
7+ years of industry experience developing and deploying ML models or 4+ years of industry experience with a PhD
Previously deployed CV/ML projects to users/customers
Strong background in deep learning for computer vision
Expert knowledge of the latest machine learning approaches for image analysis
Able to read, understand and implement the latest algorithms from research papers
Able to implement and experiment with own architecture ideas
Fluent in Python and experience with ML frameworks and models (Pytorch, Yolo, etc.)
Experience with distributed training and cloud-based ML workflows
Experience with large-scale image datasets
Preferred
Experience with pathology whole-slide images or biomedical image analysis
Familiarity with multimodal data integration (imaging + clinical / molecular data)
Expertise in industrial-scale ML engineering, including model deployment for real-time inference, GPU/throughput optimization (e.g., TensorRT, ONNX Runtime, mixed precision), and building scalable, production-ready ML pipelines with MLOps best practices
Familiarity with containerized deployments (Docker, Kubernetes) and scaling ML systems in production
Experience with CI/CD and MLOps pipelines for automated model deployment and monitoring
Track record of leading CV/ML projects from conception through deployment
Published research in the CV/ML domain