Senior ML Operations Engineer – Ads Platform

🔥 2 hours ago

🇺🇸 United States – Remote

💵 $211.4k - $248.7k / year

⏰ Full Time

🟠 Senior

⚙️ Operations

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Fetch Rewards

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2013

💼 Consulting

🍽️ Food & Beverage

📦 Logistics

💰 Debt Financing on 2022-04

Consulting • Food & Beverage • Logistics

Fetch Rewards is a company that operates America's Rewards App, leveraging the power of Fetch Points to drive consumer behavior and engagement across various industries, including Consumer Packaged Goods (CPG), retail, and restaurants. By offering custom-fit marketing solutions, Fetch helps brands increase sales, improve retention, and build lifelong consumer loyalty without relying on discounts. The platform offers dynamic in-app offers based on verified shopping behavior, designed to inspire specific business outcomes and increase customer engagement. Fetch Rewards partners with major brands and retailers, providing a robust data-driven approach to enhance buying frequency and customer retention.

📋 Description

• Own the complete model lifecycle from experimentation through validation, deployment, versioning, retraining, rollback, and retirement • Establish model promotion criteria, release processes, and rollback paths • Design and build scalable ML data and feature engineering pipelines using Spark/PySpark, S3, and Apache Iceberg • Develop incremental data processing pipelines with 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 offline and production evaluation signals, including delayed outcome labels • Use production evidence to improve model performance, platform reliability, deployment speed, and infrastructure efficiency • Build reusable training, evaluation, and deployment pipelines for multiple models and modeling approaches • Develop 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 with 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 with Data Science and Engineering and identify risks before production issues • Establish reusable engineering standards, mentor teammates, and reduce operational burden on data scientists

🎯 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: experience developing platform services, infrastructure, or internal tooling in Go • Preferred: experience building or owning a model registry, model hub, or similar system • Preferred: experience supporting ML systems with delayed outcome labels • Preferred: track record of improving deployment speed, model quality, system reliability, infrastructure cost, or manual operational effort • Preferred: experience designing configurable ML infrastructure supporting several modeling frameworks or approaches • Preferred: evidence that reusable infrastructure, standards, or documentation were adopted across multiple teams • Preferred: experience mentoring engineers or data scientists and influencing technical priorities across an ML portfolio • Preferred: experience with sensitive information and appropriate access boundaries

🏖️ Benefits

• Equity for full-time employees • 401(k) dollar-for-dollar matching up to 4% of eligible pay • Comprehensive medical, dental, and vision plans, including pets • Ten thousand per year in education reimbursement • Employee Resource Groups and Inclusion Council participation • Flexible PTO • 9 paid holidays • Year-end week-long break • 14 weeks of fully paid parental leave • 6 additional weeks of paid childbirth recovery leave for birthing parents • Flexible return-to-work schedule • One-time $2,000 Calvin Care Cash incentive for new family members • Flexible Work Environment • Hardware and software provided for remote work

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