Key skills
Machine LearningPythonSQLBig Data SolutionsProduction EngineeringReal-time Data PipelinesCross-functional CollaborationSoft SkillsMLPyTorchAnalyticsBigQueryAirflowGCPCollaboration
About this role
MNTN is a company focused on Connected TV advertising, known for its innovative approach and strong company culture. The Senior Machine Learning Engineer will operationalize machine learning models, lead the deployment and maintenance of ML solutions, and ensure that models are production-ready and scalable.
Responsibilities:
- Design and build a robust marketing platform that reaches the right audience, anywhere, anytime
- Build high volume services that are reliable at scale
- Develop big data solutions using open source frameworks
- Collaborate with and explain complex technical issues to Product and Project Leads
- Joint production ownership with shared on-call participation
- Focus on:
- Improving service reliability, latency, and observability
- Improving model quality (including false positives, thresholds, calibration)
- Speeding up model testing loops
- Releasing model and service changes faster, with confidence
- Improving data/pipeline freshness and delivery velocityDesign, train, evaluate, and improve models for deliverability, forecasting, and optimization
- Improve thresholding, calibration, and guardrail logic to reduce false positives and decision noise
- Build robust offline/online evaluation workflows tied to business outcomes
- Work directly in production codepaths to ship model improvements safely
- Partner closely with platform-leaning MLEs on reliability, rollout safety, and observability
- Share on-call responsibility for production ML services
Requirements:
- 5+ years building ML models that were deployed and operated in production
- Strong applied ML fundamentals (classification/regression/forecasting + evaluation rigor)
- 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 with Kedro, AutoGluon, PyTorch, Polars, BigQuery/GCP, and Airflow/SQLMesh ecosystems