About this opportunity
Oracle lists this Senior Machine Learning Engineer opportunity in seattle, Washington. Review the employer’s description below for duties, qualifications and application requirements.
Job description
hackajob is collaborating with Oracle to connect them with exceptional professionals for this role.
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
Minimum Job Qualifications
Education and/or Experience:
8 years of experience in data science and/or machine learning, software development, computer science, or related field
OR
Bachelor's Degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or related field AND 4 years of experience in data science and/or machine learning, software development, computer science, or related field
OR
Master's Degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or related field. AND 2 years of experience in data science and/or machine learning, software development, computer science, or related field
Job Skills:
Cloud Computing Demonstrated ability in or knowledge of cloud computing, including deploying, managing, and securing cloud environments and applications.
Code Review Demonstrated ability to conduct in-depth code reviews for software quality assurance.
Code Writing Demonstrated proficiency writing maintainable, effective code in high-level programming languages.
Predictive Analytics Demonstrated expertise in building and applying predictive analytics to identify trends and inform strategic decisions.
Machine Learning Frameworks Demonstrated ability to use machine learning frameworks to develop and optimize models for real-world business scenarios.
Programming Demonstrated proficiency in major programming languages to deliver effective software solutions.
Quality Assurance Demonstrated ability in or knowledge of quality assurance, including ensuring adherence to standards and implementing quality control measures.
Troubleshooting Demonstrated ability in or knowledge of troubleshooting, including diagnosing and resolving issues across various technical domains.
Data Security Demonstrated ability in or knowledge of data security, including applying protection principles, privacy regulations, and security protocols.
Preferred Job Qualifications
Education and/or Experience:
8 years of experience in data science and/or machine learning, software development, computer science, or related field
OR
Bachelor's Degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or related field AND 4 years of experience in data science and/or machine learning, software development, computer science, or related field
OR
Master's Degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or related field AND 2 years of experience in data science and/or machine learning, software development, computer science, or related field
OR
Doctorate in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or related field.
Job Skills:
Automation Demonstrated ability in or knowledge of automation, including designing, implementing, and managing automated tools, processes, or systems to streamline operations.
DevOps Demonstrated ability to apply CI/CD, automation, and collaboration practices to streamline software delivery.
Generative Artificial Intelligence (GenAI) Application Demonstrated experience applying GenAI techniques and prompt engineering to create realistic outputs.
Product Performance Demonstrated ability in or knowledge of product performance, including analyzing system metrics and dashboards to influence product direction.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.