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Abbott

Makes heart devices, glucose sensors and lab tests

Head of AI and Data Platform

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeAlameda, CA, USA$172k - $344k

About the Job

Requirements

  • A bachelor's degree in computer science, engineering, mathematics, or a related technical discipline is required.
  • 15+ years of progressive experience in software and data engineering, including a strong individual-contributor foundation building production AI and data systems at scale before moving into leadership.
  • Hands-on experience architecting and building large-scale data platforms, including real-time streaming pipelines, data lakes, feature stores, and machine-learning serving infrastructure on Azure, AWS, or GCP.
  • Deep system design expertise in distributed data systems, including event-driven data architectures, data models, caching strategies, and real-time biosensor data pipelines.
  • Hands-on experience engineering AI-powered products, including integrating large language models into production systems, building retrieval-augmented generation or agentic pipelines, and deploying machine-learning models in consumer-facing applications.
  • Strong understanding of AI and machine-learning system design, including model serving infrastructure, latency and cost trade-offs, evaluation frameworks, observability, data feedback loops, and safety constraints in sensitive health domains.
  • Ability to translate AI and data complexity into clear product and business narratives for C-suite and board audiences.

Responsibilities

  • Define and own technical architecture for the AI and data platform, including biosensor data ingestion pipelines, real-time and batch processing infrastructure, feature stores, model serving layers, and data lake design.
  • Drive architecture and engineering execution for AI-powered product features, including personalized metabolic health insights, predictive analytics from continuous glucose monitor data, large-language-model conversational health experiences, and on-device machine-learning inference from biosensor data.
  • Establish engineering standards for AI system design, including model integration patterns, retrieval-augmented generation pipeline architecture, prompt engineering practices, evaluation and observability frameworks, and responsible AI guardrails for a regulated health context.
  • Lead engineering execution of machine-learning model development, training, evaluation, and deployment pipelines, ensuring reliable and safe production deployment with observability to detect drift and degradation.
  • Build and own machine-learning operations infrastructure, including experiment tracking, model registry, automated retraining pipelines, A/B testing frameworks, and production model monitoring.
  • Partner with Data Science and Product teams to translate model research and product requirements into scalable, production-ready AI systems for consumer-scale reliability.
  • Define standards for large-language-model integration, including prompt management, retrieval-augmented generation, evaluation harnesses, latency and cost optimization, and safety guardrails for health-related conversational AI.
  • Ensure AI and machine-learning systems maintain alignment with HIPAA, GDPR, and FDA software guidance in partnership with Regulatory Affairs, Legal, and Quality Assurance.
  • Define, manage, and report engineering objectives and key results, key performance indicators, and delivery metrics for the AI and Data Platform function, presenting progress and insights to senior stakeholders.
  • Standardize tools, development processes, and data engineering practices across AI and data squads to improve alignment, data quality, and delivery consistency.

Desired Qualifications

  • An advanced degree in machine learning, data science, or equivalent.
  • Background in digital health, consumer health technology, or other highly regulated industries subject to HIPAA, GDPR, and FDA oversight.
  • Hands-on experience with Internet of Things or biosensor data ingestion pipelines, real-time analytics, or wearable device platforms.
  • Experience applying AI and machine learning in health or wellness, including personalization engines, anomaly detection on sensor data, or clinically informed recommendation systems.
  • Experience with IEC 62304-based software development processes for medical device software.
  • Experience in high-growth scale-up or venture-backed environments with rapid product and organizational scaling.
  • Experience defining objectives and key results, key performance indicators, and capacity planning for high-throughput, consumer-facing AI platforms with high-availability requirements.

About the Company

Abbott

Makes heart devices, glucose sensors and lab tests

Abbott makes medical devices, diagnostic tests, nutrition products and branded generic drugs for hospitals, laboratories, pharmacies and patients. Its best-known product is the FreeStyle Libre, a coin-sized sensor worn on the upper arm that tracks blood sugar around the clock without routine finger pricks. Its other lines run from heart valves, pacemakers and brain stimulators to lab analyzers, rapid tests and mail-in colon cancer screening kits, and Abbott earns money by selling these products to health systems, labs, retailers and consumers. The company was founded in 1888 by Chicago physician Wallace Abbott and is based in Abbott Park, Illinois.

Company size
10,001+
Company stage
IPO
Headquarters
Abbott Park, IL
Founded
1888

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