Join us on our journey to transform care.

Clairity is seeking a Principal Machine Learning (ML) Engineer to lead our efforts in revolutionizing healthcare through innovative medical imaging solutions. With a primary focus on advancing breast cancer screening, we are dedicated to developing cutting-edge machine learning (ML) technologies that accurately predict cancer risk, tailor personalized care plans, foster trust, and ultimately save lives. We are looking for a dynamic technical leader who shares our passion for machine learning to address real-world healthcare challenges and with strong experience of bringing state-of-the-art algorithms into production environments and monitoring ML models while in use by customers.

The Principal Machine Learning Engineer will play a pivotal role in contributing to the model development, deployment, scaling, and maintenance of advanced ML solutions, while also participating to the improvement of our ML development and production pipelines. This role will involve close collaboration with research partners, data engineers, ML engineers, software engineers, and clinical experts to deliver robust ML solutions that meet stringent regulatory standards.

The ideal candidate will possess robust expertise in developing, optimizing, deploying, and monitoring machine learning models tailored specifically for medical imaging applications. Moreover, they will demonstrate a proven track record in delivering FDA-compliant machine learning solutions within dynamic, high-pressure environments. The ideal candidate is a humble technical leader and team player with a can-do attitude, demonstrated ownership, accountability, and commitment to high quality deliverables.

Founded in 2020 by Santé Ventures and Dr. Connie Lehman, the Head of Breast Imaging at Massachusetts General Hospital, Clairity is in Austin, TX. The location of the position is flexible within the United States, with the ability to work remotely from home.

Primary Responsibilities

• Provide technical leadership and support to the ML engineers and foster a collaborative and high-performing team culture.
• Develop, deploy, and maintain state-of-the-art computer vision models for breast cancer risk prediction.
• Assist in the improvement of the Company’s ML development and production pipelines.
• Conduct model validation in collaboration with academic and clinical partners.
• Provide scientific assistance for regulatory submissions and compliance efforts.
• Contribute to peer-reviewed publications and generate Intellectual Property materials to advance scientific understanding and innovation in the field.

Requirements (Essential)

• 3+ years of experience as a technical ML engineering leader to successfully develop, deploy and deliver FDA-grade models in a regulated environment.
• 5+ years of experience in ML development within a corporate environment, preferably in the healthcare or medical imaging domain.
• 5+ years of developing Computer Vision ML models (deep learning and image processing) for image analysis, image segmentation, image classification and image prediction tasks.
• Track record of publications and innovations in ML and/or Software as a Medical Device using Machine Learning.
• Practical experience of the following technologies:
  • ML architectures: CNN, Vision Transformers, large vision models
  • ML toolkits: TensorFlow, PyTorch, Keras, scikit-learn
  • Cloud: ML Managed services, preferably on AWS
  • ML: TensorFlow, SageMaker Pipelines, TensorFlow Serving
  • Databases: data warehouse and relational databases
  • Deployment: Gitlab, Docker containers, AWS CodePipeline
  • Pipeline orchestration: AWS Step Functions, Airflow, MLFlow
  • Application exchange: REST API, JSON
  • Programming languages: Python (strong programming skills)
  • Software tools: Git, GitHub, JIRA, Confluence

Requirements (Preferred)

• Prior experience of implementing orchestration workflow solutions.
• Understanding of Software Development Life Cycle (SDLC) for software medical devices.
• Experience in version control of ML models (code, data, config, model) and model registries.
• 5+ years of experience using C++ and CUDA.

Education

• Undergraduate degree in Computer Science or Engineering. Master’s degree or PhD in computer science or engineering preferred.

For More Information, Please Send Resume To:

info@clairity.com

Clairity is seeking an experienced Software Development Engineer in Test (SDET) to help advance our mission of revolutionizing healthcare. With an initial focus on breast cancer screening, we will build mammography-based machine learning (ML) solutions that accurately predict the risk of cancer, personalize the plan of care, cultivate trust, and save lives. We want to expand our team with someone who shares our appreciation for the rapidly evolving power of big data and machine learning, and who enjoys bringing to production state-of-the-art applications to solve novel real-world healthcare problems.

We are seeking a highly skilled, experienced SDET Engineer to join our dynamic and growing team. In this role, you will work closely with our development team to design and implement testing strategies, and to ensure the robustness and reliability of our ML-powered cloud-based product.

The ideal candidate will have strong skills in software development and software test automation. He or she is a team player, highly motivated self-starter, detailed-oriented with demonstrated ownership, accountability, and commitment to high quality deliverables.

Founded in 2020 by Santé Ventures and Dr. Connie Lehman, the Head of Breast Imaging at Massachusetts General Hospital, Clairity is in Austin, TX. The location of the position is flexible within the United States, with the ability to work remotely from home.

Primary Responsibilities

• Work closely with the development team to understand the architecture of our AI-based platform and devise effective test strategies and plans.
• Design and implement Unit, Component, and Integration tests of ML infrastructure code.
• Integrate unit/component/integration/system testing into a CI/CD system (CircleCI preferred).
• Design and implement End-to-End tests (AWS Lambdas, ML Model experiments).
• Identify, analyze, and document any bugs or defects found during testing.
• Review and analyze system specifications and collaborate with developers to understand how changes in the software affect maintenance of test scripts and the testing environment.
• Implement automated tests to ensure high-quality product delivery while maintaining velocity.
• Participate in code reviews and provide input on coding standards and code improvements.
• Advocate for test priorities in daily scrum meetings and sprint planning.
• Create detailed, comprehensive, and well-structured test plans and test cases.
• Collaborate with cross-functional teams to ensure the software meets regulatory standards and requirements.

Requirements (Essential)

• 5+ years of software engineering background, with proficiency in programming languages such as Python, and experience with SW testing tools and frameworks, ML frameworks, containerization technologies, CI/CD tools, and cloud platforms, preferably AWS.
• 5+ years of experience automating component and integration tests via CI/CD infrastructure, in particular integrating unit / component / integration / system testing into a CI/CD system.
• 3+ years of experience with Agile/Scrum development methodologies.
• 3+ years of hands-on experience with big data tools and technologies such as Hadoop, Spark, and Hive, along with experience in managing and deriving insights from large, complex datasets.
• 3+ years of Data Management, including how to handle large datasets and working with different database systems.
• Proficiency in at least one machine learning framework (TensorFlow, PyTorch).
• Proficiency in Git and Python.

Requirements (Preferred)

• Proven experience as a SDET in the medical imaging or healthcare industry
• Familiarity with the concept of quality gates would be a plus.
• Knowledge of medical imaging standards: DICOM, HL7.
• Familiarity with black box and white box testing methods for Computer Vision models.

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.