Website Clairity, Inc.

Better Science Leads to Better Care

Clairity is seeking an Machine Learning Operations (MLOps) Engineer  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 algorithms to solve novel real-world healthcare problems.

The MLOps engineer will be responsible for the model deployment and continuous monitoring of our production ML solutions. As a result, he or she will implement and manage our continuous integration, continuous delivery, continuous training, and continuous monitoring pipeline and processes. The MLOps engineer will work closely with ML engineers and software engineers with the goal of deploying commercial-grade ML solutions.

The ideal candidate will have a background in ML processes and possess experience in developing automated cloud-based ML pipelines for production. The ideal candidate 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 located in Austin, TX and has raised ~$9 million in funding from investors. The location of the position is flexible within the United States, with the ability to work remotely from home.

Primary Responsibilities
Design, implement and document standardized ML processes for ML Lifecycle Management, Model Versioning and Iteration, Model Monitoring and Management, Model Governance, Model Security, and Model Discovery

– 3+ years of developing CI/CD, Continuous Training, Serving and Monitoring pipelines for ML solutions in different environments such as dev, test, staging, pre-production and production
– Experience in promoting, developing, and leveraging reusable, composable and shareable containerized components across pipelines
– Experience in version control of ML models (code, data, config, model) and model registries
– Track record of developing unit tests (components, data, models) and integration tests
– Experience of progressive delivery such as A/B testing, Canary deployment, and multi-armed bandit is desired
– Practical experience of the following technologies:
        – Cloud: ML Managed services, preferably on AWS
        – ML: TensorFlow, SageMaker Pipelines, TensorFlow Serving SageMaker Clarify, SageMaker Ground Truth
        – Databases: data warehouse and relational databases
        – Deployment: Gitlab, Docker containers, AWS CodePipeline
        – Pipeline orchestration: AWS Step Functions, Kubeflow Pipelines, Apache Airflow, MLFlow, TensorFlow Extended
        – Streaming: Kinesis Streams, Apache Kafka
        – Application exchange: REST API, JSON
        – Programming languages: Python
        – Software tools: Git, GitHub, JIRA, Confluence

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

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