Principal Machine Learning Engineer

Oracle Corporation; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Oracle Corporation is seeking a skilled ML Engineer to turn prototypes into production-ready models and automate ML workflows from ETL to deployment. The role involves collaborating with diverse teams to ensure scalable, reliable model integration and ongoing performance monitoring.

Responsibilities include building infrastructure for model performance, addressing data quality issues, and maintaining documentation and code quality.

Responsibilities

• Utilizes ML and software development knowledge to implement ML models for production.
• Transforms machine learning prototypes into production-ready models.
• Collaborates with Development Leads, Product Management, Operations, and Release Management to shape the development and delivery of software.
• Ensures ML model readiness for deployment by scaling models, cleaning model code, and meeting production quality standards.
• Automates ML workflows from data extraction, transformation, and loading (ETL) to model deployment and monitoring for CI/CD of ML solutions.
• Creates infrastructure to monitor performance and alignment with design criteria of trained models.
• Proactively monitors deployed models and troubleshoots with Data Science as needed.
• Develops novel metrics to provide analytical insights to non-technical stakeholders about model performance.
• Evaluates data quality issues (bias, fairness, data security/privacy) and mitigates their impacts on analyses and modeling.
• Collaborates with data scientists and software developers to integrate ML models into systems.
• Maintains partnership between model development and operations for smooth deployment and continuous improvement.
• Understanding operational considerations of deployment (performance, scalability, stability, maintenance).
• Provides troubleshooting and debugging support for ML infrastructure and workflows to build robust solutions.
• Develops, maintains, and refines tools, platforms, environments, and services for internal use.
• Develops clean, well-documented, medium-complexity code; follows best practices for version control, code review, and deployment.
• Builds and maintains professional documentation for processes, data collection, analyses, and model building.
• Maintains familiarity with current ML developments and third-party frameworks (e.g., PyTorch, TensorFlow, Keras).
• Keeps up-to-date with training in continuous learning and integrates new tools into production environments.
• Manages and coordinates moderately complex tasks, prioritizes work across multiple projects, and provides technical oversight.

Job description

KeyResponsibilities

MachineLearning and Data Modeling – Model Productionization

• Utilizesmachine learning (ML) and software development knowledge to implement ML modelsfor production.
• Engagesin transforming machine learning prototypes into production-ready models.
• Collaborateswith multiple stakeholders, such as Development Leads, Product Management,Operations, and Release Management, to make, adopt, and communicate technicaldecisions, and shape the development and delivery of software.

ModelDevelopment and Deployment – Model Deployment

• EnsuresML model readiness for deployment by scaling models, cleaning model code, andensuring production quality standards are met.
• Automatesmachine learning workflows, from data extraction, transformation, and loading(ETL) to model deployment and monitoring, to establish the continuousintegration and continuous delivery of machine learning solutions.

ModelDevelopment and Deployment – Model Performance

• Createsinfrastructure and frameworks to monitor the performance and alignment withdesign criteria of trained models and/or systems.
• Proactivelymonitors the performance of deployed models and troubleshoots independently orin collaboration with Data Science.
• Developsnovel metrics that provide analytical insights to non-technical stakeholders onhow well machine learning models are operating.

ModelDevelopment and Deployment – Data Quality

• Evaluatespotential issues related to data quality (e.g., bias, fairness), data security,and data privacy, and minimizes their impacts on data analyses and modeling.
• Engagesin tasks such as data cleaning, preprocessing, and feature identification toprepare for and enable model training.

InternalCollaborations and Impacts – Model Integration and Operation

• Collaborateswith multiple stakeholders (e.g., data scientists, software developers) tointegrate ML models into new or existing systems.
• Maintainsthe partnership between model development and operations, ensuring smoothdeployment and continuous improvement of ML models.
• Understandsoperational considerations of model deployment (e.g., performance, scalability,stability, maintenance).
• Providesexpert troubleshooting and debugging support, addresses issues in machinelearning infrastructure and workflow, and creates robust solutions to preventfuture problems.

InternalCollaborations and Impacts – Tool Development

• Develops,maintains, and refines tools, platforms, environments, and services forinternal use.

InternalCollaborations and Impacts – Coding and Documentation

• Developsefficient, bug-free, medium-complexity code from scratch, and properlymaintains and organizes the existing codebase.
• Implementsbest practices for version control, code review, and code delivery/deployment.
• Buildsand maintains professional documentation for technical processes(experimentation, data collection and analyses, model building).
• Testsand reviews code for bugs.

MachineLearning Expertise

• Maintainsfamiliarity with current developments in the machine learning field andintegrates knowledge into model development.
• Maintainsfamiliarity with the usage and development of third-party machine learningframeworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) tocontinuously evaluate their performance and scalability, and integrate theminto production environments.

CoreResponsibilities

Planning& Execution

• Managesand coordinates moderately complex tasks, monitoring timelines and deliverablesto ensure timely completion and adherence to requirements for a moderatelysized project or initiative.
• Efficientlydelegates, monitors, and prioritizes work across multiple projects, providingtechnical oversight and adjusting plans to address shifts in resources ortimelines.

Collaboration& Partnership

• Collaboratesacross the organization to align on expectations and achieve shared objectives.
• Leveragesunderstanding of business leaders, stakeholders, and/or customers to ensureproposed solutions meet their needs.
• Supportsinclusivity by actively seeking and listening to diverse perspectives, ensuringothers feel heard and respected.

ProblemSolving

• Identifiesand addresses moderately complex issues by analyzing a wide range of dataand/or information to identify solutions in accordance with standard practices.
• Proactivelyescalates unresolved or critical issues with a thorough assessment and suggestspotential solutions.
• Reviews,contributes to, and documents problem solving strategies.

ContinuousLearning

• Pursueslearning opportunities to expand knowledge and skills and/or tools in new areasand stays abreast of the latest industry trends and best practices.
• Proactivelyseeks and leverages ongoing feedback and training to improve skills.
• Coachesand mentors junior team members, fostering continuous learning and knowledgesharing within and across teams.

ContinuousImprovement

• Developsideas, recommends updates, and/or collaborates on the implementation of processimprovements to increase the efficiency and effectiveness of processes,protocols, and workflows across teams, and evaluates the impact on keystakeholders.
• Solicitsfeedback from others on ideas for alternative approaches and methods forcontinued improvement.

Performanceand Development

• Contributesto the talent development pipeline by participating in candidate interviews,assessing candidates, and providing hiring recommendations.