Senior Machine Learning Engineer - 1665

aKube•Santa Monica, CA
•Hybrid

About The Position

The Senior Machine Learning Engineer will design, develop, and improve production machine learning models for content classification and segmentation. The role focuses on building supervised multi-class and multi-label classifiers that assign categories, genres, and segments to content, as well as improving existing models and extending their coverage across content types. The engineer will perform exploratory data analysis, feature engineering, preprocessing, model training, tuning, and evaluation. They will work with structured and semi-structured datasets using Python, pandas, SQL, and Spark, and develop text-based and metadata-driven features. The position includes building scalable ML workflows for production, collaborating with data scientists, software engineers, and business stakeholders, and maintaining clean, modular, tested, and documented code. This is an 18-month position based in Santa Monica, California, or Seattle, Washington. The work arrangement is hybrid: onsite Monday through Thursday and remote on Friday. The stated rate is up to $103.50 per hour on W-2. Eligible work authorizations include Green Card, U.S. citizen, and valid EADs except OPT and CPT; H-1B is not accepted.

Requirements

  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.
  • At least 5 years of relevant machine learning experience, including substantial hands-on model development and production deployment.
  • Strong Python programming skills, including pandas and scikit-learn.
  • Hands-on experience with supervised machine learning, particularly classification models.
  • Strong understanding of classification algorithms, including logistic regression, decision trees, random forests, ensemble methods, and gradient-boosted models.
  • Experience building, training, evaluating, and deploying machine learning classifiers.
  • Experience with multi-class and/or multi-label classification.
  • Experience with NLP techniques and text-derived features.
  • Strong experience with feature engineering, preprocessing, and model evaluation.
  • Strong knowledge of cross-validation, model performance metrics, and hyperparameter tuning.
  • Strong SQL skills for querying and analyzing large datasets.
  • Ability to independently deliver machine learning solutions from initial data exploration through production implementation.
  • Experience with software development practices, including Git, testing, logging, and code reviews.

Nice To Haves

  • Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.
  • Experience in media, entertainment, streaming, or digital content platforms.
  • Experience building classifiers using content metadata, catalog data, or high-dimensional datasets.
  • Experience with NLP techniques such as TF-IDF, text embeddings, and semantic representations.
  • Experience with workflow orchestration tools such as Airflow or Luigi.
  • Experience with AWS or similar cloud environments.
  • Experience with search, recommendation systems, or content categorization.

Responsibilities

  • Design, develop, and improve machine learning models for content classification and segmentation.
  • Build classification models that automatically assign content categories, genres, and segments.
  • Enhance existing machine learning models and expand classification coverage across different content types.
  • Develop multi-class and multi-label classification models using supervised learning techniques.
  • Perform exploratory data analysis, feature engineering, model training, tuning, and evaluation.
  • Process structured and semi-structured datasets using Python, pandas, SQL, and Spark.
  • Develop text-based and metadata-driven features to improve classification accuracy.
  • Apply categorical encoding, normalization, feature selection, and hyperparameter optimization.
  • Build scalable machine learning workflows to support production applications.
  • Collaborate with data scientists, software engineers, and business stakeholders to implement machine learning solutions.
  • Maintain clean, modular, tested, and documented machine learning code.
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