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. Minimum Job Qualifications
Education and/or Experience:
11 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 7 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 5 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. AND 3 years of experience in data science and/or machine learning, software development, computer science, or related field.
Job Skills:
Same skills as prior level plus;
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.
Preferred Job Qualifications
Education and/or Experience:
11 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 7 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 5 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 AND 3 years of experience in data science and/or machine learning, software development, computer science, or related field.
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