Staff Machine Learning Engineer

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.

- 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

- 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

- 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