Senior ML Operations Engineer II - Ads Platform
Fetch · United States
What we’re building and why we’re building it.
Fetch helps people live rewarded every day, with a vision to become the rewards destination for everyone. We turn everyday activities into meaningful rewards, whether it’s grocery shopping, grabbing a quick meal, or playing a favorite mobile game. To date, we’ve awarded more than $1 billion in Fetch Points to our users.
Each day, more than 13 million receipts are submitted on Fetch, providing visibility into over $212 billion in gross merchandise value. This creates the largest retail-agnostic, SKU-level view of household spending, powering Fetch as an outcomes-based advertising platform that helps brands acquire and retain lifelong consumers.
The Fetch app is available on the App Store and Google Play, with more than 6 million five-star reviews from a highly engaged and loyal user base.
It’s not just our users who believe in Fetch: with investments from Softbank, ICONIQ, DST, Greycroft, and partnerships ranging from challenger brands to Fortune 500 companies, Fetch is reshaping how brands and consumers connect in the marketplace. When you work at Fetch, you play a vital role in a platform that drives brand loyalty and creates lifelong consumers with the power of Fetch points. User and partner success are at the heart of everything we do, and we extend that same commitment to our employees.
At Fetch, we value curiosity, adaptability, and the confidence to explore new tools, especially AI, to drive smarter, faster work. You don’t need to be an expert, but you should be ready to learn quickly and think critically. We welcome learners who move fast, challenge the status quo, and shape what’s next, with us. Ranked as one of America’s Best Startup Employers by Forbes for two years in a row, Fetch fosters a people-first culture rooted in trust, accountability, and innovation. We encourage our employees to challenge ideas, think bigger, and always bring the fun to Fetch.
About the RoleFetch 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.
Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. The base salary