Principal Machine Learning Engineer

Oracle•United States,

19h

About The Position

This role focuses on the productionization of machine learning models, involving the transformation of prototypes into production-ready solutions. The engineer will be responsible for ensuring ML model readiness for deployment, automating workflows, monitoring performance, and addressing data quality and security concerns. Collaboration with various stakeholders, including Development Leads, Product Management, Operations, and Release Management, is crucial for making and communicating technical decisions. The role also involves developing and maintaining internal tools, writing efficient code, implementing best practices for version control and documentation, and staying current with machine learning developments and third-party frameworks.

Requirements

Machine learning (ML) and software development knowledge
Experience transforming machine learning prototypes into production-ready models
Experience scaling models, cleaning model code, and ensuring production quality standards
Experience automating machine learning workflows (ETL, deployment, monitoring)
Experience creating infrastructure and frameworks to monitor model performance
Experience monitoring deployed models and troubleshooting
Experience evaluating data quality issues (bias, fairness, security, privacy)
Experience with data cleaning, preprocessing, and feature identification
Experience integrating ML models into new or existing systems
Understanding of operational considerations of model deployment (performance, scalability, stability, maintenance)
Experience providing troubleshooting and debugging support for ML infrastructure and workflows
Experience developing, maintaining, and refining tools, platforms, environments, and services
Experience developing efficient, bug-free code
Experience maintaining and organizing codebase
Experience implementing best practices for version control, code review, and code delivery/deployment
Experience building and maintaining professional documentation for technical processes
Experience testing and reviewing code for bugs
Familiarity with current developments in the machine learning field
Familiarity with the usage and development of third-party machine learning frameworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras)
Ability to manage and coordinate moderately complex tasks, monitoring timelines and deliverables
Ability to efficiently delegate, monitor, and prioritize work across multiple projects
Ability to provide technical oversight and adjust plans
Ability to collaborate across the organization to align on expectations and achieve shared objectives
Ability to leverage understanding of business leaders, stakeholders, and/or customers to ensure proposed solutions meet their needs
Ability to support inclusivity by actively seeking and listening to diverse perspectives
Ability to identify and address moderately complex issues by analyzing a wide range of data and/or information
Ability to proactively escalate unresolved or critical issues with a thorough assessment and suggest potential solutions
Ability to review, contribute to, and document problem solving strategies
Ability to pursue learning opportunities to expand knowledge and skills and/or tools in new areas
Ability to stay abreast of the latest industry trends and best practices
Ability to proactively seek and leverage ongoing feedback and training to improve skills
Ability to coach and mentor junior team members
Ability to develop ideas, recommend updates, and/or collaborate on the implementation of process improvements
Ability to solicit feedback from others on ideas for alternative approaches and methods for continued improvement
Ability to contribute to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations

Responsibilities

Utilizes machine learning (ML) and software development knowledge to implement ML models for production.
Transforms machine learning prototypes into production-ready models.
Collaborates 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.
Ensures ML model readiness for deployment by scaling models, cleaning model code, and ensuring production quality standards are met.
Automates 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.
Creates infrastructure and frameworks to monitor the performance and alignment with design criteria of trained models and/or systems.
Proactively monitors the performance of deployed models and troubleshoots independently or in collaboration with Data Science.
Develops novel metrics that provide analytical insights to non-technical stakeholders on how well machine learning models are operating.
Evaluates potential issues related to data quality (e.g., bias, fairness), data security, and data privacy, and minimizes their impacts on data analyses and modeling.
Engages in tasks such as data cleaning, preprocessing, and feature identification to prepare for and enable model training.
Collaborates with multiple stakeholders (e.g., data scientists, software developers) to integrate ML models into new or existing systems.
Maintains the partnership between model development and operations, ensuring smooth deployment and continuous improvement of ML models.
Understands operational considerations of model deployment (e.g., performance, scalability, stability, maintenance).
Provides expert troubleshooting and debugging support, addresses issues in machine learning infrastructure and workflow, and creates robust solutions to prevent future problems.
Develops, maintains, and refines tools, platforms, environments, and services for internal use.
Develops efficient, bug-free, medium-complexity code from scratch, and properly maintains and organizes the existing codebase.
Implements best practices for version control, code review, and code delivery/deployment.
Builds and maintains professional documentation for technical processes (experimentation, data collection and analyses, model building).
Tests and reviews code for bugs.
Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development.
Maintains 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.
Manages and coordinates moderately complex tasks, monitoring timelines and deliverables to ensure timely completion and adherence to requirements for a moderately sized project or initiative.
Efficiently delegates, monitors, and prioritizes work across multiple projects, providing technical oversight and adjusting plans to address shifts in resources or timelines.
Collaborates across the organization to align on expectations and achieve shared objectives.
Leverages understanding of business leaders, stakeholders, and/or customers to ensure proposed solutions meet their needs.
Supports inclusivity by actively seeking and listening to diverse perspectives, ensuring others feel heard and respected.
Identifies and addresses moderately complex issues by analyzing a wide range of data and/or information to identify solutions in accordance with standard practices.
Proactively escalates unresolved or critical issues with a thorough assessment and suggests potential solutions.
Reviews, contributes to, and documents problem solving strategies.
Pursues learning opportunities to expand knowledge and skills and/or tools in new areas and stays abreast of the latest industry trends and best practices.
Proactively seeks and leverages ongoing feedback and training to improve skills.
Coaches and mentors junior team members, fostering continuous learning and knowledge sharing within and across teams.
Develops ideas, recommends updates, and/or collaborates on the implementation of process improvements to increase the efficiency and effectiveness of processes, protocols, and workflows across teams, and evaluates the impact on key stakeholders.
Solicits feedback from others on ideas for alternative approaches and methods for continued improvement.
Contributes to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.