Senior Machine Learning Engineer

Description

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. Responsibilities

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.

Qualifications

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:

• Medical, dental, and vision insurance, including expert medical opinion
• Short term disability and long term disability
• Life insurance and AD&D
• Supplemental life insurance (Employee/Spouse/Child)
• Health care and dependent care Flexible Spending Accounts
• Pre-tax commuter and parking benefits
• 401(k) Savings and Investment Plan with company match

…