6 hours ago
Responsibilities
- Own the complete production model lifecycle from experimentation and validation through deployment, versioning, retraining, rollback, and retirement.
- Define model promotion criteria, release processes, rollback paths, operational standards, and MLOps priorities.
- Build monitoring for data quality, drift, prediction quality, service reliability, latency, and infrastructure cost.
- Develop reusable training, evaluation, deployment, orchestration, and model-management infrastructure for multiple models and teams.
- Create CI/CD workflows, reproducible environments, alerts, incident-response procedures, recovery mechanisms, and operational runbooks.
- Partner with data scientists to define valid evaluation signals, including for models with delayed outcome labels.
- Improve model performance, deployment speed, platform reliability, infrastructure efficiency, and operational effort using production evidence.
- Mentor teammates and establish engineering standards that reduce the 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 with Spark for distributed data processing.
- Experience with a workflow orchestration engine supporting scheduling, dependencies, 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 with model and dataset versioning, experiment tracking, lineage, and reproducible releases.
- Experience building CI/CD pipelines, monitoring, alerting, rollback mechanisms, and recovery procedures for production ML systems.
- Ability to distinguish service or infrastructure failures from changes in model effectiveness and balance model quality, delivery speed, reliability, and infrastructure cost.
- Strong cross-functional communication and experience partnering with data scientists to preserve scientific validity in production.
- Preferred experience developing platform services, infrastructure, or internal tooling in Go.
- Preferred experience owning a model registry, model hub, or configurable ML infrastructure supporting multiple frameworks or approaches.
- Preferred experience mentoring engineers or data scientists, influencing technical priorities, and working with sensitive information and access boundaries.
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About Fetch
Fetch builds a consumer rewards app where users earn points by scanning receipts and redeem them for gift cards, while brands use its SKU-level, retail-agnostic purchase data for targeted, outcomes-based advertising and measurement. The privately held company, founded in 2013, processes over 13 million receipts daily, creating visibility into more than $200 billion in gross merchandise value. Its platform has awarded over $1 billion in points and operates a hybrid-remote workplace.