Machine Learning Engineer

About The Company

Oracle is a global technology leader renowned for delivering comprehensive cloud applications and platform services that empower organizations to innovate and succeed. With a rich history of technological excellence, Oracle provides a broad spectrum of solutions spanning database management, enterprise software, and cloud infrastructure. The company's commitment to innovation, security, and customer success makes it a trusted partner for businesses worldwide. Oracle continuously invests in cutting-edge technologies and fosters a culture of continuous learning and growth, ensuring its workforce remains at the forefront of industry advancements.

About The Role

The Machine Learning Engineer role at Oracle offers an exciting opportunity to work at the intersection of data science, software engineering, and cloud technology. In this position, you will be responsible for implementing, deploying, and maintaining machine learning models in production environments. You will automate workflows, develop scalable infrastructure, and monitor model performance to ensure optimal results. Collaborating with cross-functional teams including data scientists, developers, and product managers, you will help integrate machine learning solutions into existing systems and develop new tools to enhance operational efficiency. This role requires a proactive problem-solver with a deep understanding of machine learning frameworks, data quality considerations, and production deployment challenges, all within a fast-paced, innovative environment.

Qualifications

The ideal candidate will possess a strong educational background and extensive experience in data science, machine learning, and software development. A minimum of 11 years of relevant experience is preferred, with qualifications including a Bachelor’s degree in Computer Science, Machine Learning, or related fields combined with at least 7 years of professional experience. Advanced degrees such as a Master’s or Doctorate further strengthen candidacy, especially with relevant experience. Key skills include automation, DevOps practices, proficiency with machine learning frameworks like TensorFlow, PyTorch, and Keras, and experience with GenAI techniques. Familiarity with CI/CD pipelines, system performance analysis, and security/privacy considerations in data handling is essential. The candidate should demonstrate strong problem-solving abilities, excellent collaboration skills, and a commitment to continuous learning and innovation.

Responsibilities

Key Responsibilities

Machine Learning And Data Modeling - Model Productionization

• Utilize machine learning and software development expertise to implement models suitable for production environments.
• Transform prototypes into scalable, reliable, and production-ready models, ensuring adherence to quality standards.
• Collaborate with stakeholders such as development leads, product managers, and operations teams to make informed technical decisions and facilitate smooth delivery.

Model Development And Deployment - Model Deployment

• Prepare models for deployment by scaling, optimizing code, and verifying production standards are met.
• Automate workflows covering data extraction, transformation, loading (ETL), deployment, and monitoring to enable continuous integration and delivery.

Model Development And Deployment - Model Performance

• Create infrastructure and frameworks to monitor and evaluate the performance of deployed models, ensuring alignment with design criteria.
• Proactively monitor models, troubleshoot issues, and collaborate with data science teams to enhance model accuracy and efficiency.
• Develop and communicate novel metrics that provide insights into model operation to both technical and non-technical stakeholders.

Model Development And Deployment - Data Quality

• Assess and address data quality issues such as bias, fairness, security, and privacy, minimizing their impact on modeling outcomes.
• Engage in data cleaning, preprocessing, and feature engineering to support robust model training.

Internal Collaborations And Impacts - Model Integration And Operation

• Work with multidisciplinary teams to integrate models into operational systems, ensuring scalability and stability.
• Maintain seamless collaboration between development and operational teams to support continuous improvement.
• Understand operational considerations like system performance, scalability, and maintenance, providing troubleshooting support as needed.

Internal Collaborations And Impacts - Tool Development

• Develop, maintain, and enhance internal tools, platforms, and environments to support machine learning workflows.

Internal Collaborations And Impacts - Coding And Documentation

• Write efficient, reliable, medium-complexity code from scratch and maintain existing codebases following best practices.
• Implement version control, conduct code reviews, and ensure thorough documentation of technical processes and experiments.
• Perform testing and debugging to uphold code quality standards.

Machine Learning Expertise

• Stay current with advancements in machine learning and incorporate new knowledge into model development.
• Evaluate and integrate third-party frameworks and libraries, ensuring performance and scalability in production environments.

Core Responsibilities

Planning & Execution:

• Manage and coordinate complex tasks, ensuring timely completion of projects aligned with organizational goals.
• Delegate tasks effectively, monitor progress, and adjust plans as necessary to meet deadlines.

Collaboration & Partnership

• Work cross-functionally to align expectations, share insights, and develop solutions that meet business needs.
• Engage with stakeholders to ensure solutions are inclusive and responsive to diverse perspectives.