Senior Machine Learning Engineer

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Company Overview

A leading global entertainment and streaming organization is seeking a Senior Machine Learning Engineer to join its Data & Audience Platform (DAP) — ML Engineering team. The organization develops technology that supports identity, audience intelligence, advertising, personalization, forecasting, and content discovery across a large portfolio of entertainment, news, and sports brands.

What You’ll Do

ML System Design & Technical Leadership

• Lead the end-to-end development of production machine learning systems, including data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
• Own flagship ML products such as probabilistic identity resolution, single-title affinity, lookalike modeling, or forecasting solutions.

Modeling & Experimentation

• Develop and optimize machine learning models across multiple areas, including gradient boosting, embeddings, two-tower retrieval, neural ranking, probability calibration, and probabilistic or graph-based matching.
• Design rigorous offline and online experiments.
• Establish appropriate evaluation frameworks using metrics such as precision, recall, AUC-ROC, NDCG, decile lift, and calibration curves.
• Apply causal inference techniques, including propensity scoring, uplift modeling, and incrementality modeling.

MLOps & Infrastructure

• Champion MLOps best practices, including model versioning, champion/challenger promotion, automated retraining, drift detection, and production monitoring.
• Build and maintain reproducible and auditable ML pipelines using Databricks and AWS ML services where appropriate.
• Enforce data leakage prevention and consistency between training and serving environments.
• Help define feature-store strategies, including feature contracts, backfills, and freshness SLAs.
• Implement data-quality checks, model-health dashboards, and production alerting.
• Incorporate FinOps principles into ML pipeline design through compute controls, auto-termination, and job tagging.

Agentic AI & Modern Development

• Use and advocate for AI-assisted development tools such as Cursor, GitHub Copilot, and Amazon Q.
• Leverage governed natural-language analytics capabilities to support self-service exploration of ML features and audience datasets.
• Utilize modern AI capabilities to accelerate SQL development, data discovery, and internal RAG-based tooling.
• Design and prototype agentic ML workflows using MCP-compatible tooling and frameworks such as LangChain and LangGraph.

Mentorship & Cross-functional Collaboration

• Mentor Senior and Machine Learning Engineer 2 team members through code reviews, technical discussions, and pairing.
• Help establish and maintain technical standards across the ML engineering organization.
• Serve as a technical point of contact across globally distributed teams and time zones.
• Help align priorities, resolve technical blockers, and facilitate collaboration across international engineering teams.

Required Qualifications

• 5–8 years of industry experience in machine learning engineering or applied data science, or 3+ years of experience with a Ph.D.
• Demonstrated experience leading machine learning projects through to production.
• Deep expertise in Python and strong software engineering practices.
• Production experience building and deploying machine learning systems at scale.
• Strong proficiency with Databricks, including PySpark, Delta Lake, Workflows/DLT, MLflow, and Unity Catalog.
• Strong SQL and Snowflake experience for feature sourcing and model-output delivery.
• Experience with AWS machine learning services, including SageMaker, S3, and Lambda.
• Strong understanding of model evaluation, A/B testing, and statistical or causal inference.
• Expertise in one or more areas such as recommendation systems, ranking, identity resolution, embeddings and retrieval, forecasting, or optimization.
• Demonstrated technical leadership through architectural decision-making, technical standards, and mentorship.
• Experience leading through influence across teams and time zones.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field, or equivalent professional experience.
• Excellent written and verbal communication skills.

Disclaimer

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