Senior Engineer, Machine Learning

MNTN

4h•Remote

About The Position

At MNTN, we put our people first, full stop. This allows our company culture to be defined by our team members and their shared values, like trust, ambition, quality, radical honesty, and compassionate leadership. It’s why we all really love working for the Hardest Working Software in Television™ (and also why we were named one of Ad Age’s Best Places To Work in 2025.) We pride ourselves on bringing unrivaled performance and simplicity to Connected TV advertising. Our self-serve technology makes running TV ads as easy as search and social, helping brands drive measurable conversions, revenue, site visits, and more. It’s what led MNTN to being named one of Fast Company's Most Innovative Companies in 2023. You can learn more about us and everything we do by visiting https://mountain.com/. We’re committed to innovation that empowers, not replaces. At MNTN, AI is a tool for growth, enhancing efficiency while keeping a people-first approach. Our goal is to streamline workflows and drive new solutions—without compromising the human element that makes our company great. So if wanting to do more, own more, and make a bigger impact comes naturally to you, then you may be the person we're looking for to join us in our next stage of growth. The MNTN Performance ML 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.

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.

Nice To Haves

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.

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.