Informacje o stanowisku
Workplace: Hybrid model (2 days from the office per week), Warszawa/Łódź
Workload: Full-time
Contract type: Contract of Mandate / B2B
Role Overview
We’re looking for a Machine Learning Systems Engineer to join a global leader in geospatial analytics.
In this role, you’ll be responsible for Machine Learning workflow orchestration platform – a mission-critical ecosystem that underpins the entire AI division. You’ll design, build, and maintain scalable infrastructure capable of supporting massive data and compute workloads.
Required Skills & Experience
What we offer:
- Access to LinkedIn Learning
- B2B contract + benefits
Must Have:
- Kubernetes (K8s) – Deep, production-level expertise is non-negotiable.
- Able to design and deploy clusters from scratch, manage massive workloads (10,000+ nodes).
- Cloud Experience (AWS / GCP) – Strong hands-on experience managing infrastructure in cloud environments.
- SRE / DevOps Background – Solid understanding of reliability engineering, monitoring, on-call operations, and CI/CD.
- Programming Skills (Python preferred) – Strong coding ability; Go or Rust experts open to learning Python are also welcome.
Nice-to-Have:
- Experience with MLOps platforms and lifecycle management.
- Familiarity with workflow orchestration tools (e.g., Airflow, Kubeflow).
Workplace: Hybrid model (2 days from the office per week), Warszawa/Łódź
Workload: Full-time
Contract type: Contract of Mandate / B2B
Role Overview
We’re looking for a Machine Learning Systems Engineer to join a global leader in geospatial analytics.
In this role, you’ll be responsible for Machine Learning workflow orchestration platform – a mission-critical ecosystem that underpins the entire AI division. You’ll design, build, and maintain scalable infrastructure capable of supporting massive data and compute workloads.
Required Skills & Experience
What we offer:
- Access to LinkedIn Learning
- B2B contract + benefits
,[Design and build large-scale distributed systems running on Kubernetes (up to 10,000 nodes). , Ensure platform reliability, scalability, and high availability in production environments. , Collaborate closely with AI, SRE, and Data Engineering teams to streamline model training and deployment pipelines. , Define and drive best practices in CI/CD, observability, and cloud infrastructure management. , Take ownership of incident response, on-call rotations, and reliability improvements. Requirements: Machine learning, AI, Kubernetes, Cloud, AWS, GCP, DevOps, Python, Go, Rust, MLOps, Airflow, Kubeflow Additionally: Sport subscription, Training budget, Private healthcare.
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