Software Engineer, Machine Learning

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

As a Senior Machine Learning Engineer, you will focus on operationalizing machine learning models by taking ownership of prototypes built by data scientists and turning them into robust, scalable production systems. You will lead the deployment, monitoring, and maintenance of ML solutions that power campaign optimizations at scale. This role emphasizes strong software engineering practices, designing for reliability and performance, and working with large-scale data pipelines and infrastructure. You’ll collaborate across functions to ensure models are not just accurate but production-ready, scalable, and cost-effective. This is a senior machine learning role with an emphasis on building production-ready models. It is not a pure research role. You are expected to ship production-grade implementations and own outcomes in production.

What You’ll Do:

• Design and build a robust marketing platform that reaches the right audience, anywhere and anytime
• Build high-volume services that remain reliable at scale
• 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 and decision noise
• Build offline and online evaluation workflows tied to measurable business outcomes, enabling faster testing and more confident releases
• Partner with Product, Project Leads, and platform-focused Machine Learning and Data Engineers to improve service reliability, latency, observability, and data freshness
• Share ownership of production systems, including shipping model improvements safely and participating in the on-call rotation

What Success Looks Like:

• Model quality improves on agreed business and operational metrics.
• False positives,unstable decision behavior, and other secondary metrics are reduced in key flow.
• Model testing/evaluation cycles become materially faster, better, and more performant.
• More product testing and analysis
• More model improvements reach production safely and predictably.

What You’ll Bring:

• 5+ years building ML models that were deployed and operated in production.
• Extreme Proficiency in technical communication to nontechnical stakeholders.
• Excellent applied ML fundamentals (classification/regression/forecasting + 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 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 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.