Remote ML DevOps Engineer for Scalable AI Infra

Pathway; Palo Alto (CA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Pathway is seeking a Machine Learning DevOps engineer to manage cloud and compute clusters, automate ML pipelines, and scale infrastructure for growing teams and production workloads. The role focuses on operationalizing ML models with emphasis on reliability, scalability, and automation across the ML lifecycle.

The candidate will work with Linux-based environments, container orchestration (Kubernetes, Docker), and modern CI/CD practices, across multiple cloud providers.

Qualifications

• Proficiency with Linux, shells, and cluster tooling for ML workflows.

Responsibilities

• Optimize infrastructure for ML training and inference (GPUs, distributed compute).
• Automate and maintain ML/LLM pipelines (data ingestion, training, validation, deployment).
• Manage model versioning, reproducibility, and traceability.
• Work with terabyte-scale datasets.
• Implement ML-centric CI/CD practices.
• Monitor model performance and data drift in production.
• Collaborate with ML engineers, software engineers, and platform teams.

Skills

Linux

Shell scripting

Cluster configuration

Workload management

CI/CD concepts

Python (ML)

Team collaboration

Education

BSc in Computer Science or Information Technology

Tools

Docker

Kubernetes

Slurm

Terraform

CI/CD tools (GitHub Actions, Jenkins, Gitlab CI)

ML pipeline tools (MLflow, Kubeflow, Airflow, Metaflow)

Job description

Pathway is seeking a Machine Learning DevOps engineer to manage cloud and compute clusters, automate ML pipelines, and scale infrastructure for growing teams and production workloads. The role focuses on operationalizing ML models with emphasis on reliability, scalability, and automation across the ML lifecycle.

The candidate will work with Linux-based environments, container orchestration (Kubernetes, Docker), and modern CI/CD practices, across multiple cloud providers.