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.
Requirements
You are
- 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).
- 5+ years of experience in DevOps/SRE/Platform/Infrastructure roles running production systems, ideally with high‑performance or ML workloads.
- Deep familiarity 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.
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.
Originally posted on Himalayas