ML DevOps Architect: Cloud & Large-Scale Compute (Remote)

Ignite Next GmbH; Palo Alto (CA); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Remote work

Office visits in Palo Alto, Paris, orW

Flexible relocation options

Job summary

Pathway is seeking a Machine Learning DevOps engineer to optimize ML infrastructure in the cloud, automate ML/LLM pipelines, and support scalable model deployment. The role focuses on reliable, scalable, and auditable ML lifecycle operations across distributed systems.

You will manage GPUs and distributed compute, monitor performance, and collaborate with ML engineers, software engineers, and platform teams to scale production workloads.

Qualifications

• BSc in Computer Science or Information Technology is required.
• Hands-on experience in Linux, shell scripting, and cluster management.
• Strong knowledge of Python and ML pipelines.
• Proficiency with CI/CD tools and workflows.
• Experience with ML pipeline orchestration tools (MLflow, Kubeflow, Airflow, Metaflow).
• Familiarity with cloud services across AWS/GCP/Azure and monitoring stacks.

Responsibilities

• Optimize infrastructure for ML training and inference (GPUs, distributed compute).
• Automate and maintain ML/LLM pipelines (data ingestion, training, validation, deployment).
• Manage model versioning, reproducibility, and traceability.
• Work with terabyte-scale datasets.
• Implement ML-centric CI/CD practices.
• Monitor model performance and data drift in production.

Skills

Linux

Shell scripting

Python

CI/CD

ML pipelines

Cloud platforms

Team collaboration

Education

BSc in Computer Science or Information Technology

Tools

Slurm

Docker

Kubernetes

Terraform

MLflow

Kubeflow

Airflow

Metaflow

Grafana

CloudWatch

Prometheus

Loki

Job description

Pathway is shaking the foundations of artificial intelligence by introducing the world’s first post-transformer model that adapts and thinks just like humans.

Pathway’s breakthrough architecture (BDH) outperforms Transformer and provides the enterprise with full visibility into how the model works. Combining the foundational model with the fastest data processing engine on the market, Pathway enables enterprises to move beyond incremental optimization and toward truly contextualized, experience-driven intelligence. The company is trusted by organizations such as NATO, La Poste, and Formula 1 racing teams.

Pathway is led by co-founder & CEO Zuzanna Stamirowska, a complexity scientist who created a team consisting of AI pioneers, including CTO Jan Chorowski who was the first person to apply Attention to speech and worked with Nobel laureate Goeff Hinton at Google Brain, as well as CSO Adrian Kosowski, a leading computer scientist and quantum physicist who obtained his PhD at the age of 20.

The company is backed by leading investors and advisors, including TQ Ventures and Lukasz Kaiser, co-author of the Transformer (“the T” in ChatGPT) and a key researcher behind OpenAI’s reasoning models. Pathway is headquartered in Palo Alto, California.

The opportunity

We are currently searching for a Machine Learning DevOps with experience in cloud and compute cluster management, scaling infrastructures, and Linux administration.

Our development, ML training, and production environment is in the cloud, using several major cloud providers. We need support in managing and automating the processes, and scaling the infrastructure to growing team and production needs.

You Will

• Optimize infrastructure for ML training and inference (e.g., GPUs, distributed compute).
• Automate and maintain ML/LLM pipelines (data ingestion, training, validation, deployment).
• Manage model versioning, reproducibility, and traceability.
• Work with terabyte-large datasets.
• Implement ML-centric CI/CD practices.
• Monitor model performance and data drift in production.
• Collaborate with machine learning engineers, software engineers, and platform teams.

The role focuses on operationalizing machine learning models, ensuring scalability, reliability, and automation across the ML lifecycle.

Requirements

What We Are Looking For

• Very good familiarity with Linux, shell scripts, and cluster configuration scripts as the basic work tool.
• Proficiency in workload management, containerization and orchestration (Slurm, Docker, Kubernetes).
• Solid grasp of CI/CD tools and workflows (GitHub Actions, Jenkins, Gitlab CI, etc.).
• Cloud infrastructure knowledge (AWS, GCP, Azure) – especially in ML services (e.g., SageMaker Hyperpod, Vertex AI).
• Familiarity with monitoring/logging tools (Grafana, CloudWatch, Prometheus, Loki).
• Experience with infrastructure as code (Terraform, CloudFormation, cluster-toolkit).
• Experience with ML pipeline orchestration tools (e.g., MLflow, Kubeflow, Airflow, Metaflow).
• Programming skills in Python (with exposure to ML libraries like TensorFlow, PyTorch).
• Experience with cluster, systems, and networks administration.
• Willingness to learn.

This position holds a minimum requirement of a BSc in Computer Science or Information Technology.

We will generally favor candidates who have undertaken ambitious efforts in the past. For example, if you have made an accepted contribution to the Linux kernel, won an important bug bounty, supported an academic grid/cluster computing team in a scaling effort, or even won a sports championship, make sure to mention this in your application!

Benefits

Why You Should Apply

• 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.