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MNTN is a Connected TV advertising platform that helps brands reach audiences with self-serve technology similar to search and social advertising. The Media Buying Intelligence team builds software that processes petabytes of data into campaign strategies, serving content to millions daily.
As a Senior Machine Learning Engineer, you will own the operationalization of machine learning models—taking prototypes from data scientists and transforming them into robust, scalable production systems. You will lead deployment, monitoring, and maintenance of ML solutions powering campaign optimizations at scale. This role emphasizes strong software engineering practices, reliability, performance, and large-scale data pipelines. You'll collaborate across Product, Project Leads, and platform teams to ensure models are production-ready, scalable, and cost-effective.
Key responsibilities include:
- Design and build a robust marketing platform reaching the right audience at scale
- Build high-volume services that remain reliable under load
- Develop big data solutions using open-source frameworks
- Design, train, evaluate, and improve models for deliverability, forecasting, and optimization
- Improve model quality by refining thresholds, calibration, and guardrails to reduce false positives
- Build offline and online evaluation workflows tied to measurable business outcomes
- Partner with cross-functional teams to improve service reliability, latency, observability, and data freshness
- Share ownership of production systems, ship model improvements safely, and participate in on-call rotation
Success is measured by improved model quality on agreed metrics, reduced false positives and unstable decision behavior, faster model testing cycles, increased product testing and analysis, and more model improvements reaching production safely.
REQUIREMENTS:
- 5+ years building ML models deployed and operated in production
- Extreme proficiency in technical communication to non-technical stakeholders
- Excellent applied ML fundamentals (classification, regression, forecasting with evaluation rigor)
- Strong optimization understanding in business context
- Strong Python and SQL with production engineering discipline (testing, maintainability, performance)
- Experience balancing model quality, system constraints, and speed-to-production
- Strong ownership and cross-functional collaboration experience
- Experience in ad tech, growth analytics, personalization, or performance marketing
- Proficiency with real-time or near-real-time data pipelines
- Experience with experimentation frameworks and production model monitoring
- Experience with large-scale data processing and ML systems (Kedro, AutoGluon, PyTorch, Polars, BigQuery/GCP, Airflow/SQLMesh, Databricks)
- Experience with Reinforcement Learning (Q-Learning, Multi-Armed Bandits) is a plus
