Senior Machine Learning Engineer

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. Key Responsibilities Machine Learning 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. Model Development 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. Model Development 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. Model Development 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. Internal Collaborations 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. Internal Collaborations and Impacts – Tool Development: – Contributes to the development and maintenance of tools, platforms, environments, and services for internal use. Internal Collaborations 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). Machine Learning Expertise: – Develops familiarity with current developments in the machine learning field and integrates learnings into model development. – Builds familiarity with the usage and development of third-party machine learning frameworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) to continuously evaluate their performance and scalability, and integrate them into production environments. Core Responsibilities Planning & Execution: – Independently manages work, monitoring timelines and deliverables to ensure projects or initiatives stay on track and meet requirements. – Proactively prioritizes work and adapts to resource or timeline shifts, suggesting adjustments to maintain project efficiency. Collaboration & Partnership: – Collaborates across teams to align on expectations and achieve shared objectives. – Builds and maintains a comprehensive understanding of business, stakeholder, and/or customer needs to build and support effective partnerships. – Actively listens to diverse perspectives and asks questions to ensure understanding of others. Problem Solving: – Independently identifies and addresses standard and non-standard issues in accordance with standard practices, escalating more complex issues as appropriate. – Analyzes data and/or information from multiple sources to troubleshoot standard and non-standard errors. – Contributes to knowledge sharing and best practices. Continuous Learning: – Embraces continuous learning by actively seeking to build knowledge and new skills and/or tools and staying current with industry trends and best practices. – Seeks out and leverages feedback and training to improve skills. – Contributes to a culture of continuous learning and knowledge sharing with team members. Continuous Improvement: – Develops 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. Range and benefit information provided in this posting are specific to the stated locations only US: Hiring Range in USD from: $114,600 to $234,600 per annum. May be eligible for bonus, equity, and compensation deferral. Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business. Candidates are typically placed into the range based on the preceding factors as well as internal peer equity. Oracle US offers a comprehensive benefits package which includes the following: 1. Medical, dental, and vision insurance, including expert medical opinion 2. Short term disability and long term disability 3. Life insurance and AD&D 4. Supplemental life insurance (Employee/Spouse/Child) 5. Health care and dependent care Flexible Spending Accounts 6. Pre-tax commuter and parking benefits 7. 401(k) Savings and Investment Plan with company match 8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation. 9. 11 paid holidays 10. Paid sick leave: 72 hours of paid sick leave upon date of hire. R