About the job
About Pathway
Pathway is revolutionizing artificial intelligence with the introduction of the world’s first post-transformer model that emulates human thought processes.
Our innovative architecture (BDH) surpasses traditional Transformer models, offering enterprises unparalleled insight into model functionality. By merging our foundational model with the fastest data processing engine available, Pathway empowers organizations to transcend incremental improvements and achieve truly contextualized, experience-driven intelligence. Trust from esteemed clients such as NATO, La Poste, and Formula 1 racing teams underscores our impact.
Guided by co-founder & CEO Zuzanna Stamirowska, a complexity scientist, our team includes AI trailblazers like CTO Jan Chorowski, who pioneered the application of Attention in speech and collaborated with Nobel laureate Geoffrey Hinton at Google Brain, and CSO Adrian Kosowski, a leading computer scientist and quantum physicist who earned his PhD at 20.
Headquartered in Palo Alto, California, Pathway is supported by prominent investors and advisors, including TQ Ventures and Lukasz Kaiser, co-author of the Transformer model and a key researcher behind OpenAI’s reasoning frameworks.
The Opportunity
We are seeking a talented Machine Learning DevOps Engineer with a strong background in cloud and compute cluster management, infrastructure scaling, and Linux administration. Our development, ML training, and production environments operate in the cloud, utilizing several major cloud service providers. Your role will be pivotal in managing and automating processes while scaling our infrastructure to meet the demands of our expanding team and production needs.
Your Responsibilities
- Optimize infrastructure for machine learning training and inference (e.g., GPUs, distributed computing).
- Automate and maintain ML/LLM pipelines (data ingestion, training, validation, deployment).
- Manage model versioning, ensuring reproducibility and traceability.
- Handle terabyte-scale datasets.
- Implement CI/CD practices tailored to machine learning.
- Monitor model performance and manage data drift in production.
- Collaborate with machine learning engineers, software developers, and platform teams.
This role emphasizes the operationalization of machine learning models, guaranteeing scalability, reliability, and automation throughout the ML lifecycle.
What We Are Looking For
- Strong familiarity with Linux, shell scripts, and cluster configuration as foundational tools.
- Proficiency in workload management, containerization, and orchestration (e.g., Slurm, Docker, Kubernetes).
- Solid understanding of CI/CD tools and workflows (e.g., GitHub Actions, Jenkins).
