[Hiring] Machine Learning Operations Engineer @OPENLANE

Role Description

We’re looking for a Machine Learning Operations (MLOps) Engineer who builds and operates the pipelines, platforms, and observability that carry machine learning models from experimentation into reliable, cost-efficient production. 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 independently design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions safely in production.

Qualifications

• Core knowledge of system architecture, distributed systems, cloud-native design, networking, and agile development methodologies (Scrum, Kanban).
• Functional expertise in designing end-to-end ML pipelines (data ingestion, training, validation, deployment), including requirements for latency, redundancy, scalability, and error handling.
• Technical expertise in CI/CD for ML, infrastructure-as-code, containerization and orchestration, and metrics, monitoring, and alerting toolsets.
• Working understanding of the model lifecycle: experiment tracking and lineage, model registries, model drift, data quality, and retraining triggers, with enough ML fluency to diagnose model issues alongside data scientists.
• Strong Python skills and the ability to write clean, well-tested, production-ready code.

Requirements

• 3+ years of experience in MLOps, ML engineering, Site Reliability Engineering, DevOps, or Software Engineering, including experience building or operating ML pipelines in production.
• Bachelor’s degree preferred or equivalent practical experience.
• Strong Python skills and hands-on experience with a major cloud platform (AWS preferred).
• Experience with workflow orchestration (e.g., Airflow, Kubeflow, Step Functions), infrastructure-as-code (e.g., Terraform), and CI/CD systems.
• Experience with containers and Kubernetes (Docker, EKS preferred).
• Familiarity with model serving frameworks (e.g., TorchServe, TF Serving, Triton, BentoML, SageMaker endpoints) and monitoring tools (e.g., Prometheus, Grafana, Evidently AI).
• Hands-on experience with system debugging, observability, and incident response in production environments.
• Actively uses AI development tools and can critically evaluate AI outputs.

Benefits

• Competitive pay
• Medical, dental, and vision benefits with employer HSA contributions (US) and FSA options (US)
• Immediately vested 401K (US) or RRSP (Canada) with company match
• Paid Vacation, Personal, and Sick Time
• Paid maternity and paternity leave (US)
• Employer-paid short-term disability, long-term disability, life insurance, and AD&D (US)
• Robust Employee Assistance Program
• Employer paid Leap into Service Day to volunteer
• Tuition Reimbursement for eligible programs
• Opportunities to expand your skill set and share your knowledge across a publicly traded, global organization
• Company culture of internal promotions, diverse career paths, and meaningful advancement