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Pathway

Frontier AI research for continuous learning

Machine Learning DevOps - Cloud and Compute Cluster, R&D Support

DevOps EngineeringUpdated on Nov 14
Full-timeNo salary listed

About the Job

Requirements

  • Very good familiarity with Linux, shell scripts, and cluster configuration scripts as basic work tools.
  • Proficiency in workload management, containerization, and orchestration using Slurm, Docker, and Kubernetes.
  • A solid grasp of CI/CD tools and workflows such as GitHub Actions, Jenkins, and GitLab CI.
  • Knowledge of cloud infrastructure such as AWS, GCP, and Azure, especially machine learning services such as SageMaker Hyperpod and Vertex AI.
  • Familiarity with monitoring and logging tools such as Grafana, CloudWatch, Prometheus, and Loki.
  • Experience with infrastructure as code using Terraform, CloudFormation, or cluster-toolkit.
  • Experience with machine learning pipeline orchestration tools such as MLflow, Kubeflow, Airflow, or Metaflow.
  • Programming skills in Python, with exposure to machine learning libraries such as TensorFlow and PyTorch.
  • Experience with cluster, systems, and network administration.
  • A BSc in Computer Science or Information Technology is required.

Responsibilities

  • Optimize infrastructure for machine learning training and inference, including GPUs and distributed compute.
  • Automate and maintain machine learning and large language model pipelines for data ingestion, training, validation, and deployment.
  • Manage model versioning, reproducibility, and traceability.
  • Work with terabyte-scale datasets.
  • Implement machine-learning-centric CI/CD practices.
  • Monitor model performance and data drift in production.
  • Collaborate with machine learning engineers, software engineers, and platform teams.
  • Scale and maintain cloud infrastructure to meet growing team and production needs.

Desired Qualifications

  • Candidates are generally favored for ambitious past efforts, such as an accepted contribution to the Linux kernel, winning an important bug bounty or sports championship, or supporting an academic grid or cluster computing team in a scaling effort.

About the Company

Pathway

Frontier AI research for continuous learning

Pathway conducts frontier AI research building post-transformer architectures for continuous, real-time learning. Its core BDH (Baby Dragon Hatchling) is a biologically inspired state-space model with persistent memory for specialized reasoning in finance, tech, and healthcare. BDH enables ongoing learning and real-time inference without periodic retraining, differing from standard transformer systems that rely on static weights. The goal is to scale BDH development and compute infrastructure to deploy continuous-learning, domain-specific reasoning models across industries.

Company size
51-200
Company stage
Seed
Total funding
$14.6M
Headquarters
Palo Alto, CA
Founded
2020

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