Senior Machine Learning Engineer (Hybrid)

Optomi; Seattle (WA); On-site; USD 140,000 - 190,000; Full time

Job summary

Optomi, in partnership with a leading entertainment and media organization, is seeking a Senior Machine Learning Engineer to build and deploy production-scale content classification models that assign genres, categories, and segments to titles across streaming platforms.

You will own the full ML lifecycle—from feature engineering through evaluation and implementation—collaborating with data scientists, engineers, and product teams.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field or equivalent practical experience.
• 5+ years of experience building and deploying machine learning models in production environments.
• Strong Python programming experience with libraries such as pandas and scikit-learn.
• Strong background in supervised machine learning and classification techniques.
• Experience developing multi-class and multi-label classification models.
• Hands-on expertise with feature engineering, feature preprocessing, and feature selection.
• Experience working with structured and semi-structured datasets.
• Relevant NLP experience and familiarity incorporating text-based features into machine learning models.
• Strong SQL skills with experience querying, aggregating, joining, and analyzing large datasets.
• Experience moving independently through data exploration, model training, evaluation, optimization, and implementation.
• Strong analytical and problem-solving skills with an ability to make practical modeling decisions.
• Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
• Ability to quickly ramp up on existing datasets, codebases, and business requirements in a fast-paced environment.
• Experience with AWS or other cloud-based data and compute environments is preferred.
• Media, entertainment, streaming, search, recommendation, or digital content experience is a plus.

Responsibilities

• Build, train, evaluate, and improve machine learning classification models used for global content segmentation.
• Enhance existing machine learning model families to improve content classification accuracy and coverage.
• Develop multi-class and multi-label classifiers that assign content genres, segments, and categories.
• Perform exploratory data analysis to identify meaningful features and model improvement opportunities.
• Design and implement feature engineering, preprocessing, and feature selection strategies.
• Leverage NLP techniques and text-based features to improve classification model performance.
• Work extensively with metadata, catalog data, and structured and semi-structured datasets.
• Use Python, pandas, scikit-learn, and SQL to support model development and validation efforts.
• Analyze model performance metrics and continuously optimize model effectiveness.
• Expand classification capabilities to support new content types being onboarded to streaming platforms.
• Partner with cross-functional stakeholders to understand business requirements and translate them into machine learning solutions.
• Document methodologies, model assumptions, performance results, and technical decisions.
• Support model implementation and production deployment activities.
• Participate in technical discussions, code reviews, and model evaluation sessions.
• Serve as a key contributor to content intelligence and machine learning initiatives across the organization.
• Focus primarily on hands-on machine learning development rather than analytics, dashboarding, reporting, or data engineering activities.

Skills

Python programming

Pandas

Scikit-learn

NLP

Feature engineering

SQL

Model deployment

Production ML

Education

Bachelor's degree in CS/DS/Statistics

Tools

AWS

Git

Jupyter

Job description

Optomi, in partnership with a leading entertainment and media organization, is seeking a Senior Machine Learning Engineer to support global content segmentation initiatives across a large-scale streaming ecosystem. This individual will be responsible for building, improving, and expanding machine learning classification models that automatically assign content genres, categories, and segments to titles across digital platforms. The ideal candidate brings deep hands-on experience with machine learning model development, supervised learning, NLP, feature engineering, and production-scale model deployment. This is a highly technical role focused on machine learning modeling rather than analytics, reporting, or data engineering.

What the right candidate will enjoy!!!

• Building machine learning solutions that directly impact content discovery and user experiences across global streaming platforms.
• Working on highly visible content classification and segmentation initiatives.
• Developing and improving production machine learning models rather than focusing on reporting or dashboard development.
• Collaborating with data scientists, engineers, product teams, and business stakeholders on innovative ML projects.
• Solving complex classification and NLP challenges using large-scale content and metadata datasets.
• Owning the full machine learning lifecycle from feature engineering and model development through evaluation and implementation.
• Supporting a growing portfolio of content types and helping expand model coverage across the platform.

Experience of the right candidate:

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.
• 5+ years of experience building and deploying machine learning models in production environments.
• Strong Python programming experience with libraries such as pandas and scikit-learn.
• Strong background in supervised machine learning and classification techniques.
• Experience developing multi-class and multi-label classification models.
• Hands-on expertise with feature engineering, feature preprocessing, and feature selection.
• Experience working with structured and semi-structured datasets.
• Relevant NLP experience and familiarity incorporating text-based features into machine learning models.
• Strong SQL skills with experience querying, aggregating, joining, and analyzing large datasets.
• Experience moving independently through data exploration, model training, evaluation, optimization, and implementation.
• Strong analytical and problem-solving skills with an ability to make practical modeling decisions.
• Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
• Ability to quickly ramp up on existing datasets, codebases, and business requirements in a fast-paced environment.
• Experience with AWS or other cloud-based data and compute environments is preferred.
• Media, entertainment, streaming, search, recommendation, or digital content experience is a plus.

Responsibilities of the right candidate:

• Build, train, evaluate, and improve machine learning classification models used for global content segmentation.
• Enhance existing machine learning model families to improve content classification accuracy and coverage.
• Develop multi-class and multi-label classifiers that assign content genres, segments, and categories.
• Perform exploratory data analysis to identify meaningful features and model improvement opportunities.
• Design and implement feature engineering, preprocessing, and feature selection strategies.
• Leverage NLP techniques and text-based features to improve classification model performance.
• Work extensively with metadata, catalog data, and structured and semi-structured datasets.
• Use Python, pandas, scikit-learn, and SQL to support model development and validation efforts.
• Analyze model performance metrics and continuously optimize model effectiveness.
• Expand classification capabilities to support new content types being onboarded to streaming platforms.
• Partner with cross-functional stakeholders to understand business requirements and translate them into machine learning solutions.
• Document methodologies, model assumptions, performance results, and technical decisions.
• Support model implementation and production deployment activities.
• Participate in technical discussions, code reviews, and model evaluation sessions.
• Serve as a key contributor to content intelligence and machine learning initiatives across the organization.
• Focus primarily on hands-on machine learning development rather than analytics, dashboarding, reporting, or data engineering activities.