[Remote] Senior ML Operations Engineer II - Ads Platform

Note: The job is a remote job and is open to candidates in USA. Fetch is building AI-powered systems for personalization, relevance, and ranking across its Ads Platform. The Senior MLOps Engineer will develop and operate the production infrastructure and workflows for ranking and recommendation models, partnering across Data Science, Product, Platform, and Engineering throughout the model lifecycle.

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

Skills

• 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

Benefits

• Full-time employees receive equity in Fetch.
• Fetch matches 401(k) contributions dollar for dollar, up to 4% of eligible pay.
• Comprehensive medical, dental, and vision plans for everyone, including pets.
• Ten thousand per year in education reimbursement.
• Employee Resource Groups: employee-led groups centered around fostering a diverse and inclusive workplace through events, dialogue, and advocacy; ERGs participate in the Inclusion Council with members of executive leadership.
• Flexible PTO, 9 paid holidays, and a year-end week-long break. Holidays will vary for Canada-based roles.
• 14 weeks of fully paid parental leave for all employees, plus 6 additional weeks of paid childbirth recovery leave for birthing parents, and a flexible return-to-work schedule.
• Employees welcoming new family members receive a one-time $2,000 incentive to assist with childcare, clothing, diapers, and more.
• Flexible work environment: collaborate with the team in an office or work fully remotely from anywhere in the US for US-based roles (applicable for most roles); hardware and software provided for working from home.

Company Overview

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