Principal Machine Learning Engineer

Salary: $142,000 - 194,000 per year Requirements:

• We require 11 years of experience in data science, machine learning, software development, computer science, or a related field.
• Alternatively, we require a bachelors degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or a related discipline plus 7 years of relevant experience.
• Alternatively, we require a masters degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or a related discipline plus 5 years of relevant experience.
• Alternatively, we require a doctorate in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or a related discipline plus 3 years of relevant experience.
• We value demonstrated ability or knowledge in automation, including designing, implementing, and managing automated tools, processes, or systems.
• We value demonstrated ability to apply DevOps practices, including CI/CD, automation, and cross-functional collaboration.
• We value demonstrated experience applying generative AI techniques and prompt engineering to produce realistic outputs.
• We value demonstrated ability or knowledge of product performance analysis, including using system metrics and dashboards to influence product direction.

• We implement machine learning models for production and transform prototypes into production-ready solutions.
• We partner with development leads, product management, operations, release management, data scientists, and software developers to make and communicate technical decisions.
• We ensure models are ready for deployment by scaling them, cleaning code, and meeting production-quality standards.
• We automate ML workflows across ETL, deployment, and monitoring to support continuous integration and delivery.
• We build infrastructure and frameworks to track model performance and alignment with design criteria.
• We monitor deployed models proactively and troubleshoot issues independently or with data science partners.
• We develop new metrics that help non-technical stakeholders understand model performance.
• We assess data quality, security, and privacy risks and reduce their impact on analysis and modeling.
• We handle data cleaning, preprocessing, and feature identification to prepare for model training.
• We integrate ML models into new or existing systems and maintain the connection between model development and operations.
• We address infrastructure and workflow issues, provide debugging support, and create robust fixes to prevent recurrence.
• We develop, maintain, and refine internal tools, platforms, environments, and services.
• We write efficient, bug-free code from scratch, maintain the codebase, and apply version control, code review, and deployment best practices.
• We create and maintain professional documentation for experimentation, data collection and analysis, and model building.
• We test and review code for defects.
• We stay current with developments in machine learning and incorporate new knowledge into model development.
• We evaluate third-party ML frameworks, packages, and libraries for performance and scalability in production environments.
• We manage moderately complex tasks, coordinate timelines and deliverables, and balance work across multiple projects.
• We collaborate across the organization, support inclusive teamwork, and align solutions to stakeholder and customer needs.
• We identify, analyze, and resolve moderately complex problems, escalating critical issues when needed and documenting solutions.
• We pursue continuous learning, apply feedback and training, and mentor junior team members.
• We contribute to process improvement and participate in candidate interviews, assessments, and hiring recommendations.

• AI
• CI/CD
• DevOps
• ETL
• Support
• Machine Learning
• Model Training
• Oracle
• Security
• Keras
• PyTorch
• TensorFlow

More:

We are working with Oracle through hackajob to connect with exceptional professionals for this role. This position focuses on bringing machine learning models into production, automating ML workflows, improving deployment reliability, and strengthening monitoring, troubleshooting, and internal tooling. We offer the opportunity to work closely with stakeholders across development, operations, product, and release management while contributing to scalable ML systems and continuous improvement.

last updated 37 week of 2026