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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Oracle - **Location:** Washington, DC, United States - **Experience:** Expert - **Salary:** $114,600.0 - $234,600.0 - **Contract:** Permanent contract - **Skills:** Code Review, Continuous Delivery, Continuous Integration, Data Cleansing, Extract Transform Load (ETL), Data Mining, Data Security, Machine Learning, Release Management, Software Engineering, Systems Integration, Model Validation, Machine Learning Operations, Software Version Control - **Published:** September 12, 2026 - **Apply:** https://dejobs.org/x/x/25B8BFFD75EB44868D2246B74615D6D3/job/ ## About the Role Independently identifies and addresses standard and non-standard issues in accordance with standard practices, escalating more complex issues as appropriate. ## Description Implements machine learning (ML) models for production with minimal guidance. Contributes to the readiness of machine learning models for deployment in production. Contributes to the automation of machine learning workflows. Participates in the creation of infrastructure and frameworks to monitor the performance of machine learning models in deployment. Identifies potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Contributes to addressing issues in machine learning infrastructure and workflows. Collaborates with stakeholders to integrate machine learning models into new or extant systems. Contributes to the development and maintenance of tools, platforms, and services for internal use. Develops low-complexity, efficient, bug-free code from scratch. Develops familiarity with current developments in the machine learning field and integrates knowledge into model development. Responsibilities KeyResponsibilities MachineLearning and Data Modeling - Model Productionization: - Utilizes machine learning (ML) and software development knowledge to implement ML models for production with minimal guidance. - Contributes to transforming machine learning prototypes into production-ready models. - Supports collaboration with multiple stakeholders such as Development Leads, Product Management, Operations, and Release Management to make, adopt, and communicate technical decisions, and shape the development and delivery of software. ModelDevelopment and Deployment - Model Deployment: - Contributes to ML model readiness for deployment by scaling models, cleaning model code, and ensuring production quality standards are met. - Contributes to the automation of machine learning workflows, from data extraction, transformation, and loading (ETL) to model deployment and monitoring, to establish the continuous integration and continuous delivery of machine learning solutions. ModelDevelopment and Deployment - Model Performance: - Utilizes infrastructure and frameworks to monitor the performance and alignment with design criteria of trained models and/or systems. - Monitors the performance of deployed models and troubleshoots independently or in collaboration with Data Science. - Interprets novel metrics that provide analytical insights to non-technical stakeholders on how well machine learning models are operating. ModelDevelopment and Deployment - Data Quality: - Identifies potential issues related to data quality (e.g., bias, fairness), data security, and data privacy, and contributes to minimizing their impacts on data analyses and modeling. - Contributes to tasks such as data cleaning, preprocessing, and feature identification to prepare for and enable model training. InternalCollaborations and Impacts - Model Integration and Operation: - Contributes to collaboration with multiple stakeholders (e.g., data scientists, software developers) to integrate ML models into new or existing systems. - Supports the partnership between model development and operations, ensuring smooth deployment and continuous improvement of ML models. - Learns operational considerations of model deployment (e.g., performance, scalability, stability, maintenance). - Participates in troubleshooting and debugging support efforts, such as addressing issues in machine learning infrastructure and workflow, and helping to create robust solutions to prevent future problems. InternalCollaborations and Impacts - Tool Development: - Contributes to the development and maintenance of tools, platforms, environments, and services for internal use. InternalCollaborations and Impacts - Coding and Documentation: - Contributes to the development of efficient, bug-free, low-complexity code from scratch and properly maintains and organizes the existing codebase. - Adheres to best practices for version control, code review, and continuous integration in machine learning projects. - Updates and maintains professional documentation for technical processes (experimentation, data collection and analyses, model building)., ideas and recommends updates to increase the efficiency and effectiveness of processes, protocols, and workflows within a team. - Seeks input from team members on alternative approaches and methods for improving work. Disclaimer: Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements. ## Related Videos - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Crypto-secure Data Management with In-Database Blockchain](https://www.wearedevelopers.com/videos/632-crypto-secure-data-management-with-in-database-blockchain) - [Deployed ML models need your feedback too](https://www.wearedevelopers.com/videos/161-deployed-ml-models-need-your-feedback-too) - [Build a CI/CD pipeline to automate code reviews and ensure code quality](https://www.wearedevelopers.com/videos/349-build-a-ci-cd-pipeline-to-automate-code-reviews-and-ensure-code-quality) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Dev Digest 196: AI Killed DevOps, LLM Political Bias & AI Security](https://www.wearedevelopers.com/magazine/659-dev-digest-196-ai-killed-devops-llm-political-bias-ai-security) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 150 - The shift to AI generated code, fingerprinting and OKRs vs. doing your job](https://www.wearedevelopers.com/magazine/533-dev-digest-150-the-shift-to-ai-generated-code-fingerprinting-and-okrs-vs-doing-your-job) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)