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Pathway · IT - Software Development

Senior ML Infrastructure / ML DevOps Engineer

Employment contract · Job Posted January 07, 2026
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Job Description

We are looking for a Senior ML Infrastructure / DevOps Engineer who loves Linux, distributed systems, and scaling GPU clusters more than fiddling with notebooks. You will own the infrastructure that powers our ML training and inference workloads across multiple cloud providers, from bare‑bones Linux to container orchestration and CI/CD. You will sit close to the R&D team, but your home is production infrastructure: clusters, networks, storage, observability, and automation. Your work will directly determine how fast we can train, ship, and iterate on models.

Job Responsibility

  • Design, operate, and scale GPU and CPU clusters for ML training and inference (Slurm, Kubernetes, autoscaling, queueing, quota management)
  • Automate infrastructure provisioning and configuration using infrastructure‑as‑code (Terraform, CloudFormation, cluster‑tooling) and configuration management
  • Build and maintain robust ML pipelines (data ingestion, training, evaluation, deployment) with strong guarantees around reproducibility, traceability, and rollback
  • Implement and evolve ML‑centric CI/CD: testing, packaging, deployment of models and services
  • Own monitoring, logging, and alerting across training and serving: GPU/CPU utilization, latency, throughput, failures, and data/model drift (Grafana, Prometheus, Loki, CloudWatch)
  • Work with terabyte‑scale datasets and the associated storage, networking, and performance challenges
  • Partner closely with ML engineers and researchers to productionize their work, translating experimental setups into robust, scalable systems
  • Participate in on‑call rotation for critical ML infrastructure and lead incident response and post‑mortems when things break

Requirements

  • Former or current Linux / systems / network administrator comfortable living in the shell and debugging at OS and network layers (systemd, filesystems, iptables/security groups, DNS, TLS, routing)
  • 5+ years of experience in DevOps/SRE/Platform/Infrastructure roles running production systems, ideally with high‑performance or ML workloads
  • Deep familiarity with Linux as a daily driver, including shell scripting and configuration of clusters and services
  • Strong experience with workload management, containerization, and orchestration (Slurm, Docker, Kubernetes) in production environments
  • Solid understanding of CI/CD tools and workflows (GitHub Actions, GitLab CI, Jenkins, etc.), including building pipelines from scratch
  • Hands-on cloud infrastructure experience (AWS, GCP, Azure), especially around GPU instances, VPC/networking, storage, and managed ML services (e.g., SageMaker HyperPod, Vertex AI)
  • Proficiency with infrastructure as code (Terraform, CloudFormation, or similar) and a bias toward automation over manual operations
  • Experience with monitoring and logging stacks (Grafana, Prometheus, Loki, CloudWatch, or equivalents)
  • Familiarity with ML pipeline and experiment orchestration tools (MLflow, Kubeflow, Airflow, Metaflow, etc.) and with model/version management
  • Solid programming skills in Python, plus the ability to read and debug code that uses common ML libraries (PyTorch, TensorFlow) even if you are not a full‑time model developer
  • Strong ownership mindset, comfort with ambiguity, and enthusiasm for scaling and hardening critical infrastructure for an ML‑heavy environment
  • Willingness to learn

What we offer

  • Intellectually stimulating work environment
  • Be a pioneer: you get to work with realtime data processing & AI
  • Work in one of the hottest AI startups, with exciting career prospects
  • Team members are distributed across the world
  • Responsibilities and ability to make significant contribution to the company’s success
  • Inclusive workplace culture
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