> Markdown version of [/jobs/ext/3743596-staff-ai-ml-platform-engineer](https://www.wearedevelopers.com/jobs/ext/3743596-staff-ai-ml-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Staff AI/ML Platform Engineer

- **Company:** Abbott Laboratories
- **Location:** Alameda, CA, United States
- **Experience:** Experienced
- **Salary:** $114,000.0 - $228,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Information Engineering, Extract Transform Load (ETL), DevOps, Monitoring of Systems, Python (Programming Language), Machine Learning, Reliability Engineering, Azure Machine Learning, Data Streaming, Feature Store, Cloud Platform System, Azure Data Factory, Evaluation Pipelines, Large Language Models, Prompt Engineering, Build Management, Data Lakes, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations, Drift Detection, Model Registry, Software Version Control, Docker, Databricks
- **Published:** October 15, 2026
- **Apply:** https://abbott.wd5.myworkdayjobs.com/abbottcareers/job/United-States---California---Alameda/Staff-AI-ML-Platform-Engineer_31164321-1

## About the Role

* 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.

## Description

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.