Staff AI/ML Platform Engineer

Abbott • Alameda, CA

1d • $114,000 - $228,000 • Onsite

About The Position

Abbott’s Lingo team is building a health platform that combines continuous biosensor data, real-time analytics, and personalized insights. The platform ingests millions of sensor readings daily and supports consumers and partners worldwide. The Staff AI/ML Platform Engineer will design and build infrastructure to move machine learning models from experimentation into reliable, repeatable production across cloud and device environments. The role will connect existing data lakes and ETL pipelines to ML infrastructure, enabling AI engineering and data science teams to deliver models confidently.

The engineer will work closely with data engineering, DevOps and SRE, data science, security, quality, and regulatory teams. The position is based in Alameda, California. The posted base pay range is $114,000–$228,000, with potential variation in specific locations.

Requirements

• Bachelor’s degree in Engineering, Mathematics, Computer Science, or a related field.
• At least 8 years of software or ML engineering experience, including 3 or more 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 or Kubeflow.
• Strong proficiency in Python.
• Working knowledge of containers such as Docker and 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.

Nice To Haves

• 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.

Responsibilities

• Define the architecture and roadmap for the AI platform, feature store, training pipelines, model registry, deployment, and serving.
• Partner with data engineering 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 model training and deployment through templates, tooling, and documentation.
• Extend 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, including alerting and rollback.
• Build a 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 costs 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, including HIPAA and GDPR, and produces audit-ready records.

Benefits

• Career development opportunities with an international company.
• Employees may qualify for free medical coverage in the Health Investment Plan (HIP) PPO medical plan in the next calendar year.
• Retirement savings plan with a high employer contribution.
• Tuition reimbursement.
• Freedom 2 Save student debt program.
• FreeU education benefit, offering an affordable and convenient path to a bachelor’s degree.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior