Machine Learning Engineer II, Data & Audience Platform Team

Role Overview

• Build and maintain end-to-end ML pipelines for training, evaluation, and batch inference across use cases such as identity resolution, audience segmentation, and content affinity modeling.
• Implement and experiment with supervised, unsupervised, and ranking models in Python (scikit-learn, XGBoost/LightGBM, PyTorch).
• Engineer features from first-party viewership, engagement, subscription, and behavioral signals, guarding against data leakage, collinearity, and training/serving skew.
• Run structured offline experiments; evaluate with the right metrics (precision/recall, F1, AUC-ROC, calibration, lift) and document findings in MLflow.
• Develop and maintain data and feature pipelines on Databricks (PySpark, Delta, Workflows) that feed the feature store and model-training workflows, with attention to idempotency and reproducibility.
• Write clean, tested, production-quality Python following engineering best practices (unit tests, code reviews, CI/CD).
• Use MLflow for experiment tracking, model registration, and versioning under the guidance of senior engineers.
• Support deployment and monitoring of batch inference jobs integrated with downstream activation platforms (e.g., Mosaic, FreeWheel, GAM) and data in Snowflake.
• Use AI-assisted development tools (Cursor, GitHub Copilot, Amazon Q) to accelerate coding, debugging, and documentation under guidance.
• Partner with Senior and Staff MLEs to understand system-design decisions and contribute meaningfully to technical discussions.
• Work cross-functionally with Data Engineering, Feature Engineering, and Analytics to ensure data quality and pipeline reliability.
• Document models, pipelines, and experiments clearly for team knowledge sharing.

Requirements

• 2–4 years of industry experience in machine learning, data science, or ML engineering (or 1–2 years with a relevant M.S.)
• Strong Python proficiency; experience with pandas, NumPy, scikit-learn, and at least one deep-learning framework (PyTorch or TensorFlow)
• Hands-on experience with Spark/PySpark or equivalent large-scale data processing.
• Proficiency in SQL and familiarity with cloud data warehouses/lakehouses (Snowflake or Databricks)
• Experience with experiment-tracking tools (MLflow, Weights & Biases, or similar)
• Solid grasp of core ML concepts: classification, regression, ranking, embeddings, and model evaluation; plus strong CS fundamentals (data structures, algorithms, clean code)
• Bachelor’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent practical experience)
• Ability to use AI tools to independently improve productivity across the ML lifecycle, and clear written and verbal communication.

Tech Stack

• Cloud
• Numpy
• Pandas
• PySpark
• Python
• PyTorch
• Scikit-Learn
• Spark
• SQL
• Tensorflow

Benefits

• Fast track growth opportunities
• Equal opportunity employer