Senior MLOps Engineer — Remote, Scale Production ML

OpenKrill; United States; À distance; USD 211 000 - 249 000; Plein temps

Avantages offerts par ce poste

Equity

401(k) match

Education reimbursement

Flexible work environment

Résumé du poste

Fetch is seeking an experienced ML Engineer to own the full model lifecycle from experimentation through deployment and retirement. You will partner with data scientists to productionize models, build scalable pipelines, and ensure reliability and cost efficiency across the stack.

In this role, you’ll design reusable training and deployment infrastructure, implement CI/CD for ML workloads, and mentor teammates while balancing model quality, speed, and infrastructure cost.

Qualifications

• 5+ years managing production ML systems including monitoring, retraining, incident response and continuous improvement.
• Strong production Python development skills and proficiency with SQL.
• Hands-on experience using Spark for distributed data processing.
• 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.

Responsabilités

• Own the complete ML 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.
• 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.

Connaissances

Python

SQL

Spark

Kubernetes

Terraform

CI/CD

Data pipelines

Outils

Go

Model registry

Model hub

Description du poste

Fetch is seeking an experienced ML Engineer to own the full model lifecycle from experimentation through deployment and retirement. You will partner with data scientists to productionize models, build scalable pipelines, and ensure reliability and cost efficiency across the stack.

In this role, you’ll design reusable training and deployment infrastructure, implement CI/CD for ML workloads, and mentor teammates while balancing model quality, speed, and infrastructure cost.