Leading AI Reliability Engineering Specialist
2 days ago
We are seeking an experienced engineering leader to manage our Reliability Engineering team.
This team includes Software Engineers and Systems Engineers focused on defining and achieving reliability metrics for all of our internal and external products and services.
Responsibilities:- Lead and grow a team of reliability engineers responsible for large language model serving and training systems, ensuring high-availability infrastructure capable of supporting millions of external customers and high-traffic internal workloads.
- Develop service level objectives (SLOs) that balance availability/latency with development velocity across the organization.
- Oversee the design and implementation of comprehensive monitoring systems for availability, latency, and other critical metrics.
- Guide your team in architecting high-availability language model serving infrastructure and lead the strategy for automated failover and recovery systems across multiple regions and cloud providers.
- Establish and manage incident response processes for critical AI services, ensuring rapid recovery and systematic improvements.
- Direct cost optimization initiatives for large-scale AI infrastructure, focusing on accelerator utilization and efficiency.
- Partner with cross-functional teams to align reliability engineering efforts with broader company objectives.
- Build a strong engineering culture focused on reliability, operational excellence, and innovation.
- Bachelor's degree in a related field or equivalent experience.
- Experience managing and scaling reliability or infrastructure engineering teams.
- Deep technical knowledge of distributed systems observability and monitoring at scale.
- Ability to effectively lead technical discussions while translating between ML engineers and infrastructure teams.
- Excellent leadership and communication skills, with ability to influence at all levels.
- Managed teams operating large-scale model training or serving infrastructure ( > 1000 GPUs).
- Hands-on experience with ML hardware accelerators (GPUs, TPUs, Trainium, etc.).
- Understand ML-specific networking optimizations and their operational implications.
- Led teams through major reliability transformations or infrastructure migrations.
You will have the opportunity to work with a highly skilled team and contribute to the development of cutting-edge AI technology.
Others:Please submit your application if you meet the requirements and have the necessary skills and experience.
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