Senior ML Infrastructure / ML DevOps Engineer

About Pathway

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 looking for a Senior ML Infrastructure / DevOps Engineer who loves Linux, distributed systems, and scaling GPU clusters more than fiddling with notebooks. 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 sit close to the R&D team, but your home is production infrastructure: clusters, networks, storage, observability, and automation. Your work will directly determine how fast 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.