Staff Machine Learning Engineer

Abbott Laboratories
Santa Clara, CA, United States
9 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$225,000.0 - $280,000.0
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Microsoft Azure Continuous Integration Information Engineering Extract Transform Load (ETL) Distributed Computing Environment Python (Programming Language) Machine Learning Performance Tuning Software Construction Apache Spark
+6 more
Deep Learning Model Validation Git Machine Learning Operations Data Pipelines Recurrent Neural Networks

Job description

Abbott Laboratories seeks a Senior Staff AI/ML Engineer to architect and deliver advanced machine learning solutions that power next-generation diagnostics and medical technologies. In this role, you will lead end-to-end ML development-from data engineering and model design to validation, deployment, and monitoring-within a regulated healthcare environment. Partnering with clinical, product, and regulatory teams, you will ensure safety, compliance, and real-world impact. You will mentor engineers, drive MLOps best practices, and help shape Abbott’s global AI strategy in a collaborative, inclusive culture., * Architect, build, and deploy AI/ML models for diagnostics, devices, and healthcare platforms

  • Lead end-to-end ML lifecycle including data pipelines, training, validation, and monitoring
  • Collaborate with product, clinical, and regulatory teams to meet safety and compliance standards
  • Mentor engineers and data scientists, setting best practices for ML engineering and MLOps
  • Optimize model performance, scalability, and reliability in cloud and edge environments
  • Contribute to technical strategy and roadmap for AI/ML across Abbott products

Requirements

  • Machine learning model development
  • Deep learning (e.g., CNNs, RNNs, transformers)
  • Python programming
  • Data engineering and ETL pipelines
  • MLOps and CI/CD for MLCloud platforms (AWS, Azure, or GCP)
  • Model validation and performance optimization
  • Software engineering best practices (Git, testing, code review)
  • Regulated software/medical device development (FDA, Gx
  • P)
  • Distributed computing frameworks (e.g., Spark, Ray)

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