Senior ML Operations Engineer – Ads Platform

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🕒 Outubro 30

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $211.353 - $248.650 / ano

⏰ Tempo Integral

🟠 Sênior

⚙️ Operações

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Fetch Rewards

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 2013

💼 Consultoria

🍽️ Alimentos e Bebidas

📦 Logística

💰 Debt Financing em 2022-04

Consulting • Food & Beverage • Logistics

Fetch Rewards é uma empresa que opera o aplicativo de recompensas dos Estados Unidos, aproveitando o poder do Fetch Points para impulsionar o comportamento e o engajamento do consumidor em várias indústrias, incluindo bens de consumo embalados (CPG), varejo e restaurantes. Ao oferecer soluções de marketing sob medida, a Fetch ajuda as marcas a aumentar as vendas, melhorar a retenção e construir lealdade de consumidor ao longo da vida sem depender de descontos. A plataforma oferece ofertas dinâmicas dentro do aplicativo com base no comportamento de compra verificado, projetadas para inspirar resultados de negócios específicos e aumentar o engajamento do cliente. A Fetch Rewards faz parceria com grandes marcas e varejistas, oferecendo uma abordagem robusta e orientada por dados para aumentar a frequência de compra e a retenção de clientes.

Descrição

• 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

🎯 Requisitos

• 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

🏖️ Benefícios

• 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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