Pathway: Senior ML Infrastructure / DevOps Engineer

Headquarters: Paris, Île-de-France, France

About Pathway

Pathway is revolutionizing artificial intelligence with its post-transformer model that adapts and thinks like humans.

Pathway's architecture (BDH) outperforms Transformers and provides full visibility into model operations. Combining this with the fastest data processing engine, Pathway enables enterprises to move beyond incremental optimization to experience-driven intelligence. The company is trusted by organizations like NATO, La Poste, and Formula 1 racing teams.

Pathway is led by co-founder & CEO Zuzanna Stamirowska, a complexity scientist. The team includes AI pioneers such as CTO Jan Chorowski, who applied Attention to speech and worked with Nobel laureate Goeff Hinton at Google Brain, and CSO Adrian Kosowski, a leading computer scientist and quantum physicist.

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 looking for a Senior ML Infrastructure / DevOps Engineer who enjoys Linux, distributed systems, and scaling GPU clusters. 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 work closely with the R&D team, but your focus is production infrastructure: clusters, networks, storage, observability, and automation. Your work will directly impact how quickly we can train, ship, and iterate on models.

Why This Role Is Special

Operate and scale GPU‑heavy clusters used daily by the R&D team for large‑scale training and low‑latency inference.
Design, build, and automate the ML platform rather than just run pre‑defined playbooks.
Work across multiple major cloud providers, solving interesting problems in networking, scheduling, and cost/performance optimization at scale.

You Will

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.

You Are

A 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).
Have 5+ years of experience in DevOps/SRE/Platform/Infrastructure roles running production systems, ideally with high‑performance or ML workloads.
Deeply familiar 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.

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.

To apply: https://weworkremotely.com/remote-jobs/pathway-senior-ml-infrastructure-devops-engineer