Senior Staff ML Ops Engineer

At Abbott, you can do work that matters, grow, and learn, care for yourself and family, be your true self and live a full life. You’ll also have access to:

• Career development with an international company where you can grow the career you dream of.

• Employees can qualify for free medical coverage in our Health Investment Plan (HIP) PPO medical plan in the next calendar year

• An excellent retirement savings plan with high employer contribution

• Tuition reimbursement, the Freedom 2 Save student debt program and FreeU education benefit - an affordable and convenient path to getting a bachelor’s degree.

• A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.

• A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.

This Senior Staff ML Ops Engineer position can work out of our Santa Clara, CA location.

• Architect a highly available, secure, scalable cloud/on-prem hybrid ML infrastructure that includes data ingestion, annotation, feature engineering, training, validation, deployment, and monitoring.

• Engage directly with ML scientists and act as the team’s bridge/glue between science and engineering.   

• Implement robust CI/CD workflows for ML models, including testing, rollout, rollback strategies, and compliance governance.  

• Ensure strict compliance with regulatory and privacy standards such as HIPAA, GDPR, and Software as a Medical Device (SaMD) guidelines.   

• Drive alignment and adoption of architecture strategy with business leaders. 

• Mentor and guide ML engineers and SW engineers, establish coding standards, and conduct detailed design and architectural reviews.  

• Bachelors Degree in Computer Science, Engineering Mathematics, or related field. 

• Experience working in an FDA-regulated business (e.g. validated software related to medical, pharmaceutical, or life sciences products).

• Experience with FDA 510(k) submissions and clinical-grade ML product development.

• Solid understanding of the design thinking process, as well as a passion and know-how for influencing design strategy.

• Publications, patents, or notable contributions to open-source projects.

• Background in signal processing, computer vision, or multimodal learning.

• Familiarity with data security best practices, data anonymization, synthetic data generation, and federated learning.

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