Staff AI/ML Platform Engineer

Abbott; United States; Vor Ort; USD 150.000 - 210.000; Vollzeit

Benefits dieser Stelle

Free medical coverage (HIPAA)

Excellent retirement plan

Tuition reimbursement

FreeU education benefit

Zusammenfassung

Abbott is building a Staff AI/ML Platform Engineer team in Alameda, CA, to move ML models from experiment to production across cloud and edge devices.

You will shape the platform architecture, connect data lakes and ETL with ML tooling, and partner with DevOps, SRE, data science, and security to ensure scalable, compliant ML delivery.

Qualifikationen

• Bachelor's degree in Engineering, Math, CS or related field.
• 8+ years in software/ML eng, 3+ years deploying ML on mobile/embedded devices.
• Hands-on with Azure ML SDK, Azure Data Factory, Databricks and ML tooling (MLflow, Kubeflow).
• Strong Python, containers, IaC, and at least one cloud platform.

Aufgaben

• Define architecture/roadmap for AI platform, feature store, training pipelines, and deployment.
• Collaborate with data engineering to ensure data lineage from raw sensor data to model output.
• Enable self-serve ML training/deployment via templates, tooling, and docs.
• Extend CI/CD with model-specific stages, tests, and approvals aligned with controls.
• Implement model monitoring for drift, performance, and data quality; enable rollback.
• Package/deliver models to mobile/edge devices with on-device AI engineers.
• Support LLM/agentic services, prompts, versioning, and evaluation runs.

Kenntnisse

Python

Cloud platforms

DevOps collaboration

Machine Learning

Data analysis

Ausbildung

Bachelor's degree in Engineering, Math, CS or related field

PhD or Master's in CS/ML (preferred)

Tools

Azure ML SDK

Azure Data Factory

Databricks

MLflow

Kubeflow

Docker

Kubernetes

Jobbeschreibung

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries.

JOB DESCRIPTION:

At Lingo, we're building a groundbreaking health platform that combines continuous biosensor data, real-time analytics, and personalized insights to help people live fuller, longer, and healthier lives. Our systems ingest millions of sensor readings daily, powering experiences for consumers and partners worldwide, with the reliability and scalability of cloud-native, enterprise-grade platforms.

Working at Abbott

At Abbott, you can do work that matters, grow, and learn, care for yourself and your 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 a 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 worldwide 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 the best place to work for diversity, working mothers, female executives, and scientists.

THE OPPORTUNITY

This Staff AI/ML Platform Engineer position can work out of Alameda, CA location.

As Staff AI/ML Platform Engineer, you will design and build the platform that takes machine learning models from experiment to production, safely and repeatably, across cloud and device. You will connect our existing data lake and ETL pipelines to ML infrastructure and enable both the AI engineering and data science teams to ship with confidence.

You will work closely with data engineering, DevOps and SRE, data science, security, quality, and regulatory teams, so a high level of technical judgement and professionalism is required.

What You'll Work On

• Define the architecture and roadmap for the AI platform, feature store, training pipelines, model registry, deployment, and serving.
• Partner with the data engineering team to define the boundary between curated data and ML features and ensure lineage from raw sensor data to model output.
• Enable data scientists to self-serve training and deployment through templates, tooling, and documentation.
• Extend the existing CI/CD pipelines with model-specific stages, including automated testing, validation gates, and approval workflows aligned with design controls.
• Implement model monitoring for performance, data drift, and sensor data quality on the shared observability stack, with alerting and rollback.
• Build the pipeline for packaging and delivering models to mobile and wearable devices, in partnership with the on-device AI/ML engineer.
• Support deployment and monitoring of LLM and agentic services, including prompt and model versioning and evaluation runs.
• Own training and inference cost and ML tooling decisions; partner with DevOps and SRE on infrastructure capacity, scalability, and reliability.
• Ensure the ML platform meets security, privacy, and compliance requirements (HIPAA, GDPR) and produces audit-ready records.

Qualifications

• Bachelor's degree in Engineering, Mathematics, Computer Science, or a related field.
• Minimum 8 years of software or ML engineering experience, including 3+ years deploying ML models on mobile or embedded devices.
• Hands-on experience with Azure ML SDK, Azure Data Factory, Databricks and ML tooling such as MLflow, Kubeflow.
• Strong proficiency in Python, working knowledge of containers (Docker, Kubernetes), infrastructure as code, and at least one major cloud platform.
• Experience partnering with DevOps and SRE teams on shared infrastructure.
• Experience with data lakes, streaming or time-series data, and feature stores.
• Proven experience implementing model monitoring, drift detection, and automated retraining.

Preferred Qualifications

• Ph.D. or master's degree in computer science, Machine Learning or a related field.
• Experience in a regulated industry, ideally medical devices or SaMD, including validated software environments.
• Familiarity with IEC 62304, ISO 13485, FDA guidance on AI/ML-enabled devices, and HIPAA.
• Experience deploying models to mobile or edge devices.
• Experience supporting LLM operations, such as prompt management, evaluation pipelines, and inference cost control.
• Experience with high-volume IoT or wearable sensor data.

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