Senior Ml Infrastructure - Ml DevOps Engineer H/F - 75

Pathway is building LiveAI systems that think and learn in real time as humans do. Our mission is to deeply understand how and why LLMs work, fundamentally changing the way LLMs models think.

The team is made up of AI luminaries. Pathway's CTO, Jan Chorowski, co-authored papers with Geoff Hinton and Yoshua Bengio and was one of the first people to apply attention to speech. Our CSO, Adrian Kosowski, received his PhD in Theoretical Computer Science at the age of 20 and made significant contributions across numerous scientific fields, including AI and quantum information. He also served as a professor and a coach for competitive programmers at Ecole Polytechnique. The team also includes numerous world's top scientists and competitive programmers, alongside seasoned Silicon Valley executives.

Pathway has strong investor backing. To date, we have raised over $15M;

Our offices are located in Palo Alto, CA, as well as Paris, France, and Wroclaw, Poland.About 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 barebones 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 GPUheavy clusters used daily by the R&D team for largescale training and lowlatency inference.
- Design, build, and automate the ML platform rather than just run predefined 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 infrastructureascode (Terraform, CloudFormation, clustertooling) 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 MLcentric 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 terabytescale 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 oncall rotation for critical ML infrastructure and lead incident response and postmortems when things break.