Senior Machine Learning Engineer

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 . click apply for full job details