Senior ML Operations Engineer II - Ads Platform at Fetch (United States)Senior ML Operations Engineer II - Ads Platform

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.

• 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.

• 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.

• 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.