Senior Machine Learning Engineer

We specialize in delivering unmatched performance and simplicity to Connected TV advertising. Our self-serve technology simplifies running TV ads to the ease of search and social media, driving measurable conversions, revenue, site visits, and more. This innovation earned us a spot on Fast Company’s Most Innovative Companies in 2023.

Our commitment is to innovation that empowers rather than replaces. At MNTN, AI serves as a tool for growth, enhancing efficiency while maintaining a people-first approach. Our goal is to streamline workflows and drive new solutions without compromising the human element.

If you thrive on doing more, owning more, and making a bigger impact, you may be the right fit for our next stage of growth.

The MNTN Media Buying Intelligence team helps brands reach the right customers with software that transforms petabytes of data into meaningful campaign strategies. Our engineers, data scientists, and analysts build software that serves content to millions of people daily.

Core Responsibilities:

• Design and build a robust marketing platform to reach the right audience, anywhere and anytime.
• Develop high-volume services that remain reliable at scale.
• Create big data solutions using open-source frameworks.
• Design, train, evaluate, and improve models for deliverability, forecasting, and optimization.
• Refine model quality by adjusting thresholds, calibration, and guardrails to reduce false positives and decision noise.
• Build offline and online evaluation workflows tied to measurable business outcomes, enabling faster testing and more confident releases.
• Collaborate with Product, Project Leads, and platform-focused Machine Learning and Data Engineers to enhance service reliability, latency, observability, and data freshness.
• Share ownership of production systems, including shipping model improvements safely and participating in on-call rotations.

Success Metrics:

• Improved model quality based on agreed business and operational metrics.
• Reduction of false positives, unstable decision behavior, and other secondary metrics in key flows.
• Materially faster, better, and more performant model testing/evaluation cycles.
• Increased product testing and analysis.
• More model improvements reaching production safely and predictably.

Required Qualifications:

• 5+ years of experience building ML models deployed and operated in production.
• Extreme proficiency in technical communication to nontechnical stakeholders.
• Strong applied ML fundamentals including classification, regression, forecasting, and rigorous evaluation.
• Strong understanding of optimization in a business context.
• Strong Python and SQL skills with production engineering discipline (testing, maintainability, performance).
• Experience balancing model quality, system constraints, and speed-to-production.
• Strong experience with ownership and cross-functional collaboration.
• Experience in ad tech, growth analytics, personalization, or performance marketing.
• Proficiency working 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 such as: Kedro, AutoGluon, PyTorch, Polars, BigQuery/GCP, Airflow/SQLMesh, and Databricks ecosystems.
• Experience in Reinforcement Learning such as Q-Learning or Multi-Armed Bandits is a plus.