Senior ML Operations Engineer – Monetization

🔥 17 minutes ago

🇺🇸 United States – Remote

💵 $211.4k - $248.7k / year

⏰ Full Time

🟠 Senior

⚙️ Operations

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Fetch

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2013

👥 B2C

📣 Marketing

🔥 Funding within the last year

💰 Later Stage VC on 2026-05

B2C • Marketing

Fetch is a consumer mobile rewards app that lets users scan receipts to earn points that can be redeemed for gift cards and other rewards. The app awards points for everyday purchases (groceries, fuel, meals, games, etc. ), offers in-app games and promotions, and provides a business-facing offering (Fetch for Business) and browser extension. Fetch reports millions of monthly earners, millions of app reviews, and over $1B in rewards redeemed. The service emphasizes easy receipt scanning, point accumulation, and low-effort rewards for shoppers.

📋 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 • 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-label use cases • 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 for 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 for 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 engineering standards, mentor teammates, and reduce operational burden on data scientists

🎯 Requirements

• 5+ years managing 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 using Spark for distributed data processing • 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 or quality measures • Preferred: Track record 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 of reusable infrastructure, standards, or documentation adopted across multiple teams • Preferred: Experience mentoring engineers or data scientists and influencing technical priorities • Preferred: Experience with sensitive information and appropriate access boundaries

🏖️ Benefits

• Equity for full-time employees • 401(k) matching dollar for dollar up to 4% of eligible pay • Comprehensive medical, dental, and vision plans, including pets • $10,000 per year in education reimbursement • Employee Resource Groups • 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 employees welcoming new family members • Flexible work environment • Hardware and software equipment for home-based work

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