Principal Machine Learning Engineer at Eeho

About The Role

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

–Utilizes
machine learning (ML) and software development knowledge to implement ML models
for production.
–Engages
in transforming 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.

Model

Development and Deployment – Model Deployment

–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.

Model

Development and Deployment – Model Performance

–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.

Model

Development and Deployment – Data Quality

–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.

Internal

Collaborations and Impacts – Model Integration and Operation

–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.

Internal

Collaborations and Impacts – Tool Development

–Develops,
maintains, and refines tools, platforms, environments, and services for
internal use.

Internal

Collaborations and Impacts – Coding and Documentation

–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.

Machine

Learning Expertise

–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.

Core

Responsibilities

Planning

& Execution

–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.

Collaboration

& Partnership

–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.

Problem

Solving

–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.

Continuous

Learning

–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.

Continuous

Improvement

–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.

Performance

and Development

–Contributes
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

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
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.
11 paid holidays
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.
Paid parental leave
Adoption assistance
Employee Stock Purchase Plan
Financial planning and group legal
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.