Senior Manager, Machine Learning Engineering - Ad Platform

Fetch•Remote,

3d•Remote

About The Position

Fetch is looking for a Senior Manager of Machine Learning Engineering to lead the team building and scaling machine learning capabilities across our Ad Platform. You will partner with Product, Data Science, Engineering, and business stakeholders to translate strategy into a measurable roadmap that improves advertiser outcomes, member experience, and marketplace performance. You will lead engineers through ambiguous, business-critical problems while maintaining a strong balance between product delivery, model quality, reliability, scalability, and long-term technical health.

Requirements

8+ years of experience in software engineering, machine learning engineering, or a related technical field, including 2+ years managing and developing engineering teams.
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
Experience managing and developing machine learning or software engineers in a product-focused environment.
Strong technical background building and operating production machine learning systems at scale.
Experience translating business and product objectives into technical roadmaps and measurable outcomes.
Strong understanding of the ML lifecycle, including data quality, feature development, training, evaluation, deployment, monitoring, and iteration.
Experience making technical trade-offs involving model quality, latency, scalability, reliability, and maintainability.
Ability to lead teams through medium-to-high ambiguity and complex cross-functional dependencies.
Demonstrated experience coaching senior engineers and raising technical and operational standards.
Strong communication and stakeholder-management skills, including the ability to influence without direct authority.
Proficiency with Python and SQL and experience with modern machine learning frameworks and cloud-based data or ML systems.
Experience establishing accountability for both delivery outcomes and the long-term technical health of owned systems.

Nice To Haves

Master’s degree or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, or a related technical field.
Experience building machine learning systems for advertising, recommendations, ranking, personalization, or marketplace optimization.
Familiarity with ad auction dynamics, bidding, targeting, inventory forecasting, attribution, and campaign measurement.
Experience leading teams responsible for low-latency, high-scale production systems.
Strong understanding of experimentation, causal inference, and incrementality measurement.
Experience with modern MLOps practices, feature platforms, model monitoring, and automated training and deployment pipelines.
Experience working with large-scale data processing and distributed systems.
Demonstrated success leading cross-team technical initiatives in a rapidly evolving product environment.

Responsibilities

Lead and develop a team of machine learning engineers responsible for business-critical Ad Platform systems.
Translate product and technical strategy into quarterly and annual roadmaps with measurable product, technical, and delivery outcomes.
Guide the development of ML solutions for areas such as ad ranking, targeting, bidding, inventory forecasting, optimization, and measurement.
Partner with Product, Data Science, Analytics, and Engineering teams to define success metrics, experimentation strategies, and technical priorities.
Make sound trade-offs across delivery speed, model performance, scalability, reliability, maintainability, and technical debt.
Raise the engineering bar through strong design reviews, code reviews, operational ownership, and architectural standards.
Proactively identify technical and organizational risks before they constrain delivery or platform growth.
Coach engineers on system design, technical decision-making, execution, and career development.
Use data, experiments, incidents, system performance, and delivery metrics to guide priorities and improve team effectiveness.
Drive alignment and execution across teams with shared systems, goals, and dependencies.