Pathway

Senior ML Infrastructure – DevOps Engineer

Pathway

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇫🇷 France

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Job Level

Senior

Tech Stack

AirflowAWSAzureCloudDNSDockerGoogle Cloud PlatformGrafanaJenkinsKubernetesLinuxPrometheusPythonPyTorchShell ScriptingTensorflowTerraform

About the role

  • 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 who is 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.
  • What we are looking for
  • 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.
Benefits
  • 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’ success
  • Inclusive workplace culture
  • Further details
  • - **Type of contract**: Permanent employment contract
  • - **Preferable joining date**: Immediate.
  • - **Compensation**: based on profile and location.
  • - **Location**: Remote work. Possibility to work or meet with other team members in one of our offices: Palo Alto, CA; Paris, France or Wroclaw, Poland. Candidates based anywhere in the EU, United States, and Canada will be considered.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
GPU clustersCPU clustersML pipelinesinfrastructure as codeCI/CDshell scriptingworkload managementcontainerizationprogramming in Pythonmonitoring and logging
Soft skills
strong ownership mindsetcomfort with ambiguityenthusiasm for scalinglead incident responsecollaboration with ML engineers