Lead Senior Principal Machine Learning Engineer | Oracle Health

Do you want to be a part of changing healthcare?

Oracle is excited to be using our resources, knowledge, and expertise—as well as our successes in other industries—and applying them to healthcare to make a meaningful impact. As people, we all participate in healthcare, it’s deeply personal, and we put the human at the center of each of our decisions. Improving healthcare for all requires bringing unique perspectives and expertise together to holistically tackle the biggest problems in global health including physician burnout, patient access to data, and barriers to quality care.

Oracle Health Applications & Infrastructure (OHAI) is developing patient- and provider-centric solutions rapidly and securely. We leverage the power of Oracle Cloud Infrastructure (OCI) to deliver robust, scalable solutions across patient, provider, payer, public health, and life sciences sectors. At OHAI, you’ll work with experts across industries and have access to cutting-edge technologies. We apply artificial intelligence, machine learning, large language models, learning networks, and data intelligence in an applied, scalable, and embedded way. Join us in creating people-centric healthcare experiences.

About the Team:

As part of the Oracle Health Foundations Organization, you’ll join a high-impact team focused on using machine learning and intelligent automation to improve the performance and reliability of Oracle Health's cloud platforms. We're building systems that detect anomalies, predict incidents, and enable proactive intervention at scale.

As a Senior Machine Learning Engineer, you will play a leading role in shaping the technical direction of our ML systems. You'll be responsible for not only designing and building production-grade models and infrastructure, but also mentoring others, influencing architectural decisions, and driving cross-team collaboration. This is a high-ownership, high-visibility role where you will combine your engineering acumen with applied ML expertise to solve business-critical problems.

Responsibilities:

• Design and lead the implementation of machine learning systems that detect anomalies, predict incidents, and enable autonomous reliability features in Oracle Health platforms.
• Develop scalable software services that integrate ML models into observability and operations pipelines.
• Own the full ML lifecycle, including data ingestion, model training, validation, deployment, monitoring, and iteration using MLOps best practices.
• Guide architecture decisions around ML infrastructure, data pipelines, model serving, and observability integrations.
• Partner with cross-functional stakeholders (engineering, SRE, product, operations) to align technical solutions with strategic objectives.
• Mentor and provide technical direction to junior engineers and data scientists on modeling, code quality, and deployment best practices.
• Stay current with emerging technologies in ML engineering, anomaly detection, time series, and reliability automation—and lead efforts to evaluate and integrate them.

Requirements:

• 10+ years of experience in applied machine learning, with at least 3+ years in an engineering-heavy role deploying ML models to production at scale.
• Strong software development and system design skills in Python; experience building APIs, services, and production systems.
• Expertise in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Proven experience architecting and deploying models in cloud environments (OCI, AWS, GCP, or Azure), with knowledge of CI/CD, containerization, and orchestration (e.g., Docker, Kubernetes).
• Deep knowledge of time series modeling, anomaly detection, and telemetry data (logs, metrics, traces).
• Proficiency in SQL and distributed data tools (e.g., Spark, BigQuery, Flink, or similar).
• Demonstrated ability to drive technical strategy and influence cross-functional direction.
• Bachelor's, Master’s, or PhD in Computer Science, Machine Learning, or a related field preferred.

Skills:

• Machine Learning System Design & Deployment
• MLOps, CI/CD Pipelines, Model Monitoring
• Production-Grade Software Engineering (Python, APIs, Services)
• Anomaly Detection & Forecasting with Observability Data
• Scalable Data Pipelines (Streaming and Batch)
• Cloud-Native Architecture (OCI, AWS, GCP)
• Distributed Systems & Data Engineering (Spark, Kafka, etc.)
• Cross-Team Technical Leadership
• Strong Communication & Influence Skills

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