---
title: 'Senior ML Operations Engineer II - Ads Platform at Fetch'
canonical: 'https://feeny.ai/job/senior-ml-operations-engineer-ii-ads-platform-fetch-united-states-b4mtpemjn1rd'
type: 'job'
last_seen: '2026-10-26'
---

# Senior ML Operations Engineer II - Ads Platform at Fetch

- **Company:** Fetch
- **Location:** United States
- **Compensation:** $211k–$249k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-22
- **Last confirmed live:** 2026-10-26
- **Apply:** https://jobs.gem.com/fetch/am9icG9zdDqNf_MF7acyLd6K6uPXgjfo

## Job description

## About the Role

Fetch is entering its AI-first era, building the intelligent systems that power personalization, relevance, and ranking across our Ads Platform. These systems help Fetch deliver more relevant, adaptive, and high-performing experiences for millions of users while creating measurable value for advertisers.

As a Senior MLOps Engineer, you’ll own the operational foundation that brings ranking and recommendation models from experimentation into reliable production systems. You’ll partner with Data Science, Product, Platform, and Engineering across the full model lifecycle, including reproducible training, validation, deployment, versioning, monitoring, retraining, and retirement.

This is a hands-on technical leadership role operating at the intersection of ML infrastructure, personalization, and large-scale distributed systems. You’ll build reusable pipelines, strengthen observability and real-time learning workflows, establish architectural and reliability standards, and make model health and progress accessible through a shared model hub. Your work will enable Fetch’s Ads Platform to become smarter, faster, and more responsive to changing user behavior over time.

## Role Responsibilities

- Own the complete model lifecycle from experimentation through validation, deployment, versioning, retraining, rollback, and retirement.
- Establish clear model promotion criteria, release processes, and rollback paths that enable teams to ship improvements confidently.
- Design and build scalable ML data and feature engineering pipelines, including feature generation, historical aggregations, and training dataset construction, using Spark/PySpark and modern lakehouse technologies such as S3 and Apache Iceberg.
- Develop reliable, incremental data processing pipelines with support for data quality validation, lineage, point-in-time correctness, and training-serving feature consistency.
- Build monitoring for data quality, drift, prediction quality, service reliability, latency, and infrastructure cost.
- Partner with data scientists to define meaningful offline and production evaluation signals, including for use cases with delayed outcome labels.
- Use production evidence to improve model performance, platform reliability, deployment speed, and infrastructure efficiency.
- Build reusable training, evaluation, and deployment pipelines that support multiple models and modeling approaches.
- Develop dependable orchestration workflows covering dependencies, scheduling, retries, backfills, and failure recovery.
- Translate scientific requirements into maintainable production systems with clear interfaces, ownership, and operational expectations.
- Build and maintain an accessible model hub connecting model documentation, evaluation results, release history, versioning, and monitoring.
- Create tested CI/CD workflows, reproducible environments, actionable alerts, incident-response procedures, and operational runbooks.
- Define MLOps priorities in partnership with Data Science and Engineering, identifying risks before they become production issues.
- Establish reusable engineering standards, mentor teammates, and reduce the operational burden placed on data scientists.

## Minimum Requirements

- 5+ years of experience building and operating production ML systems beyond initial deployment, including monitoring, retraining, incident response, and continuous improvement.
- Strong production software development skills in Python and proficiency with SQL.
- Hands-on experience with Spark/PySpark for large-scale distributed data processing, including feature engineering, historical aggregations, incremental processing, and training dataset construction.
- Experience with modern data lake or lakehouse architectures, including S3, Apache Iceberg, or equivalent technologies.
- Experience with a workflow orchestration engine for scheduling, dependency management, retries, backfills, and recovery.
- Hands-on experience building infrastructure with Terraform.
- Experience deploying and operating containerized production workloads on Kubernetes.
- Experience building reusable training, evaluation, or deployment infrastructure used by multiple models, scientists, or teams.
- Experience implementing model and dataset versioning, experiment tracking, lineage, and reproducible releases.
- Ability to distinguish service or infrastructure failures from changes in model effectiveness.
- Experience building CI/CD pipelines, monitoring, alerting, rollback mechanisms, and recovery procedures for production ML systems.
- Demonstrated ability to balance model quality, delivery speed, reliability, and infrastructure cost.
- Strong cross-functional communication and experience partnering with data scientists to preserve scientific validity as models move into production.
- Ability to explain a production incident or model degradation issue, how it was detected, and what improvements followed.

## Preferred Requirements

- Experience developing platform services, infrastructure, or internal tooling in Go.
- Experience building or owning a model registry, model hub, or similar system used by technical and nontechnical stakeholders.
- Experience supporting ML systems where outcome labels are delayed or model quality cannot be measured immediately.
- Track record of improving deployment speed, model quality, system reliability, infrastructure cost, or manual operational effort.
- Experience designing configurable ML infrastructure that supports several modeling frameworks or approaches.
- Evidence that reusable infrastructure, standards, or documentation were adopted across multiple teams.
- Experience mentoring engineers or data scientists and influencing technical priorities across an ML portfolio.
- Experience working in environments with sensitive information and designing documentation or monitoring around appropriate access boundaries.

## About Fetch

## Company Overview
- **One-liner**: Fetch is a rewards app and outcomes-based advertising platform that gives users points for everyday shopping, dining, and gaming activities, redeemable for gift cards and other rewards.
- **Entity Type**: Private (Series E)
- **Headquarters**: Madison, Wisconsin, United States
- **Founded**: 2013
- **Founders**: Wes Schroll

## Core Business
- **Primary industry/industries**: Consumer rewards, digital advertising, mobile apps, data analytics
- **Target customers**: B2C (consumers via the Fetch app) and B2B (brands and retailers via its outcomes-based advertising platform)
- **Mission or purpose statement**: To create the rewards destination for everyone, turning everyday moments into opportunities for consumers to earn something back while helping brands acquire and retain lifelong consumers.

## Products & Services
- **Fetch Rewards App (Consumer)**: A mobile app that awards users "Fetch Points" for submitting receipts from any retailer, as well as for dining out, playing games, and other daily activities. Points can be redeemed for free gift cards to popular retailers, restaurants, and charities. The app personalizes offers and becomes more rewarding the more it's used.
- **Fetch for Business (Advertising Platform)**: An outcomes-based advertising platform that gives brands SKU-level visibility into over $212 billion in annual gross merchandise value. It helps brands target shoppers with purchase-based offers, drive incremental sales, and build lasting brand loyalty through a non-disruptive, reward-based ad model.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed (private company)
- **Key Metric**: Over $855.3 million in total funding across 13 rounds; annual revenue estimated at $102 million [linkedin.com](https://linkedin.com/company/fetch-rewards)
- **Notable Investors/Partners**: Not explicitly listed in search results, but the company has raised significant capital (Series E stage) from venture capital firms.
- **Growth Signals**: Employs 792 people (with a -5.6% YoY change) [linkedin.com](https://linkedin.com/company/fetch-rewards); processes over 13 million receipts daily; has visibility into $212 billion in annual GMV; named one of America’s Best Startup Employers by Forbes for two consecutive years [fetch.com](https://fetch.com/careers/jobs); has offices or coworking spaces in Madison, Chicago, Birmingham, Boston, New York, and Mexico City.

## Competitive Advantages
- **Massive, proprietary data set**: Fetch possesses the largest retail-agnostic, SKU-level view of household spending, giving it unique consumer insights that power its advertising platform.
- **Mutually beneficial model**: Unlike disruptive ads, Fetch’s model creates a win-win where consumers get rewards and brands get measurable, outcomes-based results.
- **Scale and adoption**: With millions of active users and over 13 million receipts submitted daily, Fetch has a large, engaged user base that is difficult for competitors to replicate.
- **Third-party validated measurement**: The platform’s effectiveness is rigorously measured and validated by industry-leading third parties, building trust with brand partners.

## Strategic Focus
- **Continued platform expansion**: The company is focused on growing its rewards ecosystem beyond just receipt scanning, including dining, gaming, and other daily moments.
- **Deepening advertising capabilities**: Fetch is investing in its outcomes-based ad platform to help brands acquire and retain consumers more effectively.
- **AI and data science**: The company is actively hiring for roles like Staff Product Designer, AI Labs and Senior Data Scientist, indicating a focus on leveraging AI to enhance personalization and product innovation [fetch.com](https://fetch.com/careers/jobs).
- **International and talent growth**: With offices in Mexico City and remote roles, Fetch is expanding its geographic footprint and talent base.

## Why Work Here
- **Culture and recognition**: Ranked as one of America’s Best Startup Employers by Forbes for two years in a row, with a people-first culture rooted in trust, accountability, and innovation [fetch.com](https://fetch.com/careers/jobs).
- **Generous compensation and benefits**:
    - **Equity**: All employees receive Restricted Stock Units (RSUs), giving them a stake in the company’s success [fetch.com](https://fetch.com/careers/faq).
    - **401(k)**: 100% match on the first 4% of contributions [fetch.com](https://fetch.com/careers).
    - **Parental Leave**: 20 weeks for primary caregivers, 14 weeks for secondary caregivers, plus a $2,000 stipend [fetch.com](https://fetch.com/careers).
    - **Education**: $10,000+ per year in education reimbursement for job-related courses or programs [fetch.com](https://fetch.com/careers).
    - **Family Planning**: Support for egg retrieval, adoption, IVF, and surrogacy through partner Carrot, with a $10k lifetime stipend [fetch.com](https://fetch.com/careers).
    - **Health & Wellness**: Medical, Dental, Vision (with perks like teeth whitening and laser eye surgery discounts), HSA with employer contributions, wellness coach, nutrition counseling, and Gympass membership [fetch.com](https://fetch.com/careers/faq).
    - **Time Off**: Flexible PTO, 9 paid holidays, plus a year-end week-long break [fetch.com](https://fetch.com/careers).
    - **Pet Insurance**: Fully covered for the first pet, and bereavement leave includes furry family members [fetch.com](https://fetch.com/careers/faq).
- **Flexible work environment**: Most roles offer a "Remote Flexible Environment," with offices in Madison, Chicago, Birmingham, Boston, New York, and Mexico City for in-person collaboration [fetch.com](https://fetch.com/careers/faq).
- **Early career programs**: Offers a 12-week Tech Internship and a 12-week Tech Apprenticeship program for those starting their careers in software engineering or data [fetch.com](https://fetch.com/careers/faq).
- **Hiring process**: Typically includes an application review, a take-home exercise, a phone interview, and a panel interview [fetch.com](https://fetch.com/careers/faq).

## Sources
1. [fetch.com - Careers Page](https://fetch.com/careers)
2. [fetch.com - Careers FAQ](https://fetch.com/careers/faq)
3. [fetch.com - About Us](https://business.fetch.com/about-us)
4. [fetch.com - Open Positions](https://fetch.com/careers/jobs)
5. [linkedin.com - Fetch Company Page](https://linkedin.com/company/fetch-rewards)

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