Remote MLOps Engineer: Scale ML Pipelines

OPENLANE; Oregon; Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401K match

Paid time off

Parental leave

Disability insurance

Employee assistance

Job summary

OPENLANE seeks an MLOps Engineer to build and operate pipelines, platforms, and observability for production ML models. You will join a new MLOps team within Site Reliability Engineering and help define how OPENLANE trains, ships, monitors, and safely updates models at scale.

You will design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions reliably in production, with a remote-friendly, globally collaborative setup.

Qualifications

• 3+ years in MLOps, ML engineering, Site Reliability Engineering, DevOps, or Software Engineering.
• Bachelor's degree or equivalent practical experience.
• Strong Python skills and hands-on cloud platform experience (AWS preferred).
• Experience with workflow orchestration (Airflow, Kubeflow, Step Functions).
• IaC and CI/CD for ML (Terraform, CI pipelines).
• Containers and Kubernetes (Docker, EKS).
• Familiarity with model serving frameworks and monitoring tools.
• Hands-on debugging, observability, and incident response in prod.

Responsibilities

• Design, build, and operate automated ML pipelines for ingestion, training, validation, deployment, and rollback.
• Manage Model CI/CD with automated testing, canary releases, and rollback strategies.
• Monitor model performance, data drift, inference latency, and availability with actionable alerts.
• Ensure reproducibility via versioned code, data, features, and environments.

Skills

Python

Cloud platforms

CI/CD

Kubernetes

Data pipelines

Observability

AI tooling

Education

Bachelor's degree

Tools

Airflow

Kubeflow

Step Functions

Terraform

Docker

Kubernetes

SageMaker

TorchServe

Job description

OPENLANE seeks an MLOps Engineer to build and operate pipelines, platforms, and observability for production ML models. You will join a new MLOps team within Site Reliability Engineering and help define how OPENLANE trains, ships, monitors, and safely updates models at scale.

You will design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions reliably in production, with a remote-friendly, globally collaborative setup.