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Senior ML Infrastructure / ML DevOps Engineer

15 hours, 11 minutes ago
Europe, United States, Canada
Full-time
Senior
DevOps Engineer
DevOps and Infrastructure
Apache Airflow AWS Azure CI/CD CloudFormation DNS Docker GCP GitHub Actions GitLab CI Grafana Jenkins Kubeflow Kubernetes Linux MLflow Prometheus Python PyTorch Shell Scripting TensorFlow Terraform TLS Vertex AI
Apply Now
Pathway Genomics

Pathway Genomics

Pathway Genomics is a global leader in genetic testing and personalized healthcare, integrating AI and deep learning for actionable precision health information worldwide.

Health Care Providers & Services
51-250
Founded 2008
$40M raised
View All Jobs 8

Description

  • Design, operate, and scale GPU and CPU clusters for ML training and inference using tools such as Slurm, Kubernetes, autoscaling, queueing, and quota management.
  • Automate infrastructure provisioning and configuration with infrastructure-as-code and configuration management tools.
  • Build and maintain ML pipelines for data ingestion, training, evaluation, deployment, reproducibility, traceability, and rollback.
  • Implement and evolve ML-focused CI/CD pipelines for testing, packaging, and deploying models and services.
  • Own monitoring, logging, and alerting for training and serving workloads, including utilization, latency, throughput, failures, and data or model drift.
  • Work with terabyte-scale datasets and solve related storage, networking, and performance challenges.
  • Partner with ML engineers and researchers to productionize experimental work into robust, scalable systems.
  • Participate in the on-call rotation for critical ML infrastructure and lead incident response and post-mortems.

Requirements

  • 5+ years of experience in DevOps, SRE, Platform, or Infrastructure roles running production systems, ideally with high-performance or ML workloads.
  • Former or current Linux, systems, or network administrator comfortable debugging at the OS and network layers.
  • Deep familiarity with Linux as a daily driver, including shell scripting and cluster and service configuration.
  • Strong experience with workload management, containerization, and orchestration in production environments, including Slurm, Docker, and Kubernetes.
  • Solid understanding of CI/CD tools and workflows such as GitHub Actions, GitLab CI, or Jenkins, including building pipelines from scratch.
  • Hands-on cloud infrastructure experience with AWS, GCP, or Azure, especially GPU instances, VPC/networking, storage, and managed ML services.
  • Proficiency with infrastructure as code tools such as Terraform or CloudFormation, with a bias toward automation.
  • Experience with monitoring and logging stacks such as Grafana, Prometheus, Loki, or CloudWatch.
  • Familiarity with ML pipeline and experiment orchestration tools such as MLflow, Kubeflow, Airflow, or Metaflow, and model/version management.
  • Solid programming skills in Python and the ability to read and debug code using common ML libraries such as PyTorch or TensorFlow.
  • Strong ownership mindset, comfort with ambiguity, and enthusiasm for scaling and hardening critical infrastructure in an ML-heavy environment.
  • Willingness to learn.

Benefits

  • Intellectually stimulating work environment.
  • Opportunity to work with real-time data processing and AI.
  • Work at a leading AI startup with strong career prospects.
  • Distributed team with members across the world.
  • Opportunity to make a significant contribution to the company’s success.
  • Inclusive workplace culture.
  • Permanent employment contract.
  • Remote work, with the option to meet team members in Palo Alto, Paris, or Wroclaw.
  • Candidates based anywhere in the EU, United States, and Canada will be considered.
  • Compensation based on profile and location.

Interested in this position?

Apply directly on the company website

Apply Now

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