Senior Machine Learning Engineer

Machine Learning Engineers are the partners for the growth, content, marketing, product, and engineering team. They leverage data, statistical methods, and machine learning to generate model outputs, predictions, and scalable solutions that inform critical decisions and shape the experiences of millions of viewers worldwide.

Through model development, analysis, experimentation, and data products, they build capabilities that are continuously refined through close collaboration with cross- functional stakeholders.

As a Senior Machine Learning contractor on the segmentation data science team, you will

develop machine learning solutions for content classification and segmentation. You will work

with large-scale content metadata and related data to develop features, train and evaluate

classifiers, and build scalable workflows that generate segments for downstream applications.

This is a highly hands-on role requiring strong applied machine learning expertise, proficiency in

Python and modern data science tools, and the ability to independently take solutions from data

exploration and feature development through model evaluation and implementation.

Key Responsibilities

● Design, develop, evaluate, and implement machine learning classifiers for content

classification and segmentation use cases.

● Explore, analyze, and prepare content datasets using Python, pandas, SQL, Spark, and

related data science tools.

● Develop features from structured and semi-structured metadata, including categorical,

numerical, text-derived, and aggregated features.

● Build robust feature-processing workflows, including missing-value handling, categorical

encoding, scaling, normalization, feature selection, and other appropriate

transformations.

● Develop multi-class and multi-label classification models using appropriate supervised

machine learning techniques, and train, tune, and compare models using cross-

validation, and hyperparameter optimization,

● Partner with data scientists, engineers, and content stakeholders to translate

requirements into practical machine learning solutions.

● Apply strong software development practices, including modular code, version control,

testing, logging, code review, and documentation.

Basic Qualifications

● Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics,

Engineering, or a related quantitative field, or equivalent practical experience.

● 5+ years of industry experience building and deploying machine learning models in

production environments.

● Strong proficiency in Python, with hands-on experience using pandas and scikit-learn for

data preparation and machine learning.

● Strong understanding of supervised machine learning and classification techniques,

including linear models, decision trees, random forests, ensemble methods, and

gradient-boosted models.

● Strong experience with feature engineering, feature preprocessing, and feature selection

for structured and semi-structured datasets.

● Experience developing multi-class and/or multi-label classification models.

● Strong SQL skills and experience querying, joining, aggregating, and analyzing large

datasets.

● Ability to independently move from exploratory data analysis and feature development

through model training, evaluation, and implementation.

● Strong analytical and problem-solving skills, with the ability to make pragmatic modeling

decisions based on data, project requirements, and delivery constraints.

● Strong written and verbal communication skills, with the ability to clearly explain

technical approaches and results to technical and non-technical stakeholders.

● Ability to operate effectively in a fast-paced environment, ramp up quickly on existing

data and code, and independently deliver within a project-based contract engagement.

Preferred Qualifications

● Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related

field.

● Experience developing classifiers from metadata, catalog data, or other high-

dimensional structured and semi-structured datasets.

● Experience incorporating text-based features using techniques such as TF-IDF, text

embeddings, or other semantic representations.

● Experience working with media, entertainment, streaming, search, recommendation, or

other digital content domains.

● Familiarity with workflow orchestration tools such as Luigi, Airflow, or similar frameworks.

● Experience with cloud-based data and compute environments, particularly AWS.