Overview

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Platform Engineer in Europe.

Responsibilities

• Deploy, manage, and optimize machine learning models and data pipelines on Kubernetes (EKS) and cloud infrastructure.
• Collaborate with AI researchers to support infrastructure requirements and facilitate research-to-production workflows.
• Implement and maintain Infrastructure-as-Code (Terraform/Terragrunt) for provisioning and scaling cloud resources.
• Build and manage CI/CD pipelines to automate model and application deployment.
• Monitor, troubleshoot, and enhance system performance using observability tools (e.g., Datadog).
• Manage workflow orchestration platforms (e.g., Temporal.io) to streamline model and data lifecycles.
• Partner with finance and operations teams to integrate cost management and vendor oversight (FinOps).

Requirements

• Proven experience with cloud-based hosting, preferably AWS, and container orchestration platforms like Kubernetes (EKS).
• Strong knowledge of Infrastructure-as-Code frameworks such as Terraform and Terragrunt.
• Experience building and managing CI/CD pipelines and deployment workflows.
• Proficiency with scripting languages such as Python and operational support of production systems.
• Familiarity with observability and monitoring platforms (e.g., Datadog) for large-scale infrastructure.
• Ability to work collaboratively with AI researchers and engineering teams in an asynchronous, remote-first environment.
• Strong problem-solving skills, adaptability in dynamic environments, and attention to detail.
• Preferred: experience with high-volume SaaS products, workflow orchestration platforms (Temporal.io), and frontend or full-stack application support.

Benefits

• Competitive base salary starting at EUR/GBP/CHF 100,000 + stock options.
• Fully remote role within the EU, UK, or Switzerland with flexible work arrangements.
• 25 days of leave plus local holidays.
• Opportunity to work on cutting-edge AI and machine learning infrastructure.
• Collaborative, fast-paced, and growth-oriented work environment.
• Exposure to proprietary AI technology and direct collaboration with world-class AI researchers.