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ClearML

The Infrastructure Platform For AI Builders: Maximize AI Potential at Enterprise Scale

Senior Python Systems Engineer, Agent & Infrastructure

Systems EngineerSystems EngineerFull TimeRemoteSeniorTeam 11-50Since 2016H1B No SponsorCompany SiteLinkedIn

Location

Germany

Posted

98 days ago

Salary

0

Seniority

Senior

Job Description

Senior Python Systems Engineer, Agent & Infrastructure

ClearML

• Agent Development: Design and optimize the clearml-agent, a Python service responsible for pulling jobs, setting up environments, and executing ML pipelines. • Kubernetes Integration: Write logic to interact directly with K8s APIs, manage Pod life-cycles, and handle Custom Resource Definitions (CRDs). • Resource Management: Implement logic for dynamic resource allocation (GPU/CPU/Memory) and container orchestration. • Systems Programming: Build robust daemons and services that interact with OS-level primitives (systemd, signals, I/O streams). • Networking: Troubleshoot and optimize TCP/IP connections, DNS resolution, and firewall traversal to ensure seamless connectivity for users.

Job Requirements

  • 8+ years of development experience with a strong focus on Systems Programming.
  • Kubernetes Mastery: Deep understanding of Kubernetes architecture (beyond just writing YAML). You should know how to write code that controls K8s.
  • Container Internals: Extensive experience with Docker, including building and maintaining images.
  • Python for Systems: Experience using Python for automation, daemons, or CLI tools (using libraries like subprocess, socket, asyncio).
  • Networking Fundamentals: Strong grasp of HTTP/S, WebSockets, TCP/IP, Proxies, and Reverse Proxies.
  • OS Knowledge: strong understanding of Linux internals and shell scripting.

Benefits

  • Fully Remote & Global – Work from anywhere with a distributed team of top-tier engineers.
  • Engineering-First & Autonomous – High ownership, real responsibility, and freedom to design and ship impactful solutions.
  • High Growth, High Impact – Your work directly affects thousands of users, from startups to large enterprises.
  • Technically Deep Challenges – Build complex, performance-critical systems at the core of modern AI infrastructure.
  • Fast Feedback, Real Users – See your work in production quickly and make a measurable difference.

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