Principal Machine Learning Engineer

Implements machine learning (ML) models for production. Ensures the readiness of machine learning models for deployment in production. Automates machine learning workflows. Creates infrastructure and frameworks to monitor the performance of machine learning models in deployment. Evaluates potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Addresses issues in machine learning infrastructure and workflows. Collaborates with stakeholders to integrate machine learning models into new or extant systems. Develops, maintains, and refines tools, platforms, and services for internal use. Develops efficient, bug-free code from scratch. Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development.

Key

Responsibilities

Machine

Learning and Data Modeling - Model Productionization:

margin-left:.5in;text-indent:-.25in'>-Utilizes

machine learning (ML) and software development knowledge to implement ML models

for production.

margin-left:.5in;text-indent:-.25in'>-Engages

in transforming machine learning prototypes into production-ready models.

margin-left:.5in;text-indent:-.25in'>-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.

Model

Development and Deployment - Model Deployment:

margin-left:.5in;text-indent:-.25in'>-Ensures

ML model readiness for deployment by scaling models, cleaning model code, and

ensuring production quality standards are met.

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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:

margin-left:.5in;text-indent:-.25in'>-Creates

infrastructure and frameworks to monitor the performance and alignment with

design criteria of trained models and/or systems.

margin-left:.5in;text-indent:-.25in'>-Proactively

monitors the performance of deployed models and troubleshoots independently or

in collaboration with Data Science.

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novel metrics that provide analytical insights to non-technical stakeholders on

how well machine learning models are operating.

Model

Development and Deployment - Data Quality:

margin-left:.5in;text-indent:-.25in'>-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.

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in tasks such as data cleaning, preprocessing, and feature identification to

prepare for and enable model training.

Internal

Collaborations and Impacts - Model Integration and Operation:

margin-left:.5in;text-indent:-.25in'>-Collaborates

with multiple stakeholders (e.g., data scientists, software developers) to

integrate ML models into new or existing systems.

margin-left:.5in;text-indent:-.25in'>-Maintains

the partnership between model development and operations, ensuring smooth

deployment and continuous improvement of ML models.

margin-left:.5in;text-indent:-.25in'>-Understands

operational considerations of model deployment (e.g., performance, scalability,

stability, maintenance).

margin-left:.5in;text-indent:-.25in'>-Provides

expert troubleshooting and debugging support, addresses issues in machine

learning infrastructure and workflow, and creates robust solutions to prevent

future problems.

Internal

Collaborations and Impacts - Tool Development:

margin-left:.5in;text-indent:-.25in'>-Develops,

maintains, and refines tools, platforms, environments, and services for

internal use.

Internal

Collaborations and Impacts - Coding and Documentation:

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efficient, bug-free, medium-complexity code from scratch, and properly

maintains and organizes the existing codebase.

margin-left:.5in;text-indent:-.25in'>-Implements

best practices for version control, code review, and code delivery/deployment.

margin-left:.5in;text-indent:-.25in'>-Builds

and maintains professional documentation for technical processes

(experimentation, data collection and analyses, model building).

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and reviews code for bugs.

Machine

Learning Expertise:

margin-left:.5in;text-indent:-.25in'>-Maintains

familiarity with current developments in the machine learning field and

integrates knowledge into model development.

margin-left:.5in;text-indent:-.25in'>-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.

Core

Responsibilities

Planning

& Execution:

margin-left:.5in;text-indent:-.25in'>-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.

margin-left:.5in;text-indent:-.25in'>-Efficiently

delegates, monitors, and prioritizes work across multiple projects, providing

technical oversight and adjusting plans to address shifts in resources or

timelines.

Collaboration

& Partnership:

margin-left:.5in;text-indent:-.25in'>-Collaborates

across the organization to align on expectations and achieve shared objectives.

margin-left:.5in;text-indent:-.25in'>-Leverages

understanding of business leaders, stakeholders, and/or customers to ensure

proposed solutions meet their needs.

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inclusivity by actively seeking and listening to diverse perspectives, ensuring

others feel heard and respected.

Problem

Solving:

margin-left:.5in;text-indent:-.25in'>-Identifies

and addresses moderately complex issues by analyzing a wide range of data

and/or information to identify solutions in accordance with standard practices.

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escalates unresolved or critical issues with a thorough assessment and suggests

potential solutions.

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contributes to, and documents problem solving strategies.

Continuous

Learning:

margin-left:.5in;text-indent:-.25in'>-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.

margin-left:.5in;text-indent:-.25in'>-Proactively

seeks and leverages ongoing feedback and training to improve skills.

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and mentors junior team members, fostering continuous learning and knowledge

sharing within and across teams.

Continuous

Improvement:

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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.

margin-left:.5in;text-indent:-.25in'>-Solicits

feedback from others on ideas for alternative approaches and methods for

continued improvement.

Performance

and Development:

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to the talent development pipeline by participating in candidate interviews,

assessing candidates, and providing hiring recommendations.

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: $126,200 to $264,100 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. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.

11. Paid parental leave

12. Adoption assistance

13. Employee Stock Purchase Plan

14. Financial planning and group legal

15. Voluntary benefits including auto, homeowner and pet insurance

The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.

As part of Oracle's onboarding process and consistent with applicable law, US-based employees are required to complete identity verification, which involves the collection and processing of their biometric information. Accommodations to this requirement may be granted following an individualized assessment.