about 5 hours ago
Palo Alto, CA, USASenior
Responsibilities
- Design and operate end-to-end ML infrastructure including training orchestration and automated evaluation pipelines.
- Own LLM/SLM serving infrastructure to scale low-latency, high-throughput inference.
- Build and manage multi-GPU training and inference clusters across cloud and on-prem environments.
- Implement observability for models in production with actionable alerting.
- Apply inference-time optimizations in collaboration with ML and Research Engineers.
- Set MLOps best practices and tooling standards as a senior member of the infrastructure team.
Requirements
- 5+ years of experience in MLOps, ML infrastructure, or platform engineering with production ownership.
- Proven experience deploying and scaling LLM inference infrastructure in production.
- Strong proficiency with Kubernetes, Docker, and infrastructure-as-code tools like Terraform.
- Hands-on experience with GPU cluster management and distributed training environments.
- Proficient in Python with a track record of building maintainable systems.
- Experience with ML pipeline and orchestration tools such as Airflow or Kubeflow.
- Solid foundation in computer science fundamentals and cloud architecture.
Benefits
- Work with a team from top-tier institutions and companies.
- Opportunity to shape the infrastructure of a pioneering company.
- Competitive salary, significant equity, and premium benefits.