# Senior Director, Machine Learning

**Company:** [Fetch](https://hotfix.jobs/companies/fetch)
**Location:** Remote
**Role:** Engineering Management
**Experience:** 10+ years
**Skills:** Machine Learning, Ads Delivery, Ranking, Relevance, Forecasting, Pacing, Personalization, Optimization, Recommendations, A/B Testing, Model Monitoring, MLOps, Real-Time Serving, Feature Platforms, Distributed Data Processing
**Posted:** 2026-05-11

> Leads strategy and execution for ML systems powering ads delivery, ranking, personalization, and optimization on Fetch's rewards platform. Requires 10+ years in ML/data science, 5+ years leading high-scale teams, and deep expertise in ads/recommendations systems.

## Job Description

## Role Responsibilities
- Own the machine learning strategy and execution for Fetch’s ads and offers optimization domain.
- Lead teams responsible for ads delivery, ads infrastructure, ranking, relevance, forecasting, pacing, personalization, and optimization.
- Define technical direction across multiple teams, systems, and stakeholders.
- Translate ambiguous business problems into clear technical strategies, system-level decisions, and measurable execution plans.
- Ensure architectural coherence, platform reliability, model quality, and operational excellence across the domain.
- Partner with Product, Engineering, Data Science, Analytics, Sales, and business stakeholders to align ML investments with user experience, advertiser performance, and company growth.
- Lead trade-off decisions involving relevance, pacing, yield, monetization, marketplace dynamics, latency, scalability, and long-term platform health.
- Coach and develop managers, technical leads, and senior ICs to raise standards for technical judgment, execution, and team effectiveness.
- Build scalable mechanisms for planning, hiring, performance management, technical review, and accountability.
- Own domain-level outcomes across technical quality, product progress, business impact, delivery performance, and organizational health.

## Minimum Requirements
- 10+ years of experience in machine learning, software engineering, data science, or a related technical discipline.
- 5+ years of experience leading teams, managers, or technical leaders in a high-scale technology environment.
- Experience operating ML, ranking, ads, recommendations, personalization, or optimization systems at scale.
- Deep experience with ads delivery, ranking, relevance, personalization, forecasting, pacing, marketplace quality, or optimization.
- Strong technical foundation developed through prior hands-on experience as an engineer, ML practitioner, applied scientist, data scientist, or technical lead.
- Proven ability to define technical direction across multiple teams, including system architecture, platform capability, and engineering leverage.
- Strong judgment around ML system design, experimentation, model evaluation, ranking quality, marketplace dynamics, and technical trade-offs.
- Experience influencing senior stakeholders and aligning cross-functional partners around technical priorities and business outcomes.
- Experience coaching managers, technical leads, and senior ICs.
- Track record of building inclusive, high-performing teams with strong ownership, engagement, and execution discipline.

## Preferred Requirements
- Experience leading ads delivery, ad ranking, ad relevance, ads infrastructure, or ads marketplace teams at a scaled consumer technology, retail media, marketplace, social, search, gaming, or ad tech company.
- Experience with large-scale recommendations, personalization, retrieval and ranking systems, auction dynamics, yield optimization, or real-time decisioning platforms.
- Experience with forecasting, pacing, budget optimization, campaign delivery, or performance prediction.
- Experience working in a two-sided or multi-sided marketplace.
- Familiarity with experimentation platforms, A/B testing, offline and online evaluation, causal measurement, model monitoring, and production ML operations.
- Experience with modern ML tooling, feature platforms, real-time serving systems, distributed data processing, and cloud-based infrastructure.
- Experience partnering with go-to-market or commercial teams on advertiser, brand, or partner-facing outcomes.
- Experience scaling systems, teams, and processes in a high-growth environment.

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