Sr Staff Engineer, ML Hardware Infrastructure and Performance
Description For Sr Staff Engineer, ML Hardware Infrastructure and Performance
LinkedIn, the world's largest professional network, is seeking a Senior Staff Engineer to lead their ML Hardware Infrastructure and Performance team. This role represents a unique opportunity to shape the future of AI infrastructure at scale.
The position focuses on designing and maintaining large-scale GPU infrastructure for machine learning and AI workloads. As the technical leader, you'll be responsible for crucial decisions regarding hardware selection, architecture design, and operational excellence of LinkedIn's ML platform.
The role requires deep expertise in GPU computing, high-performance networking, and distributed systems. You'll work with cutting-edge technologies including latest GPU architectures, high-speed interconnects, and advanced storage systems. The position demands both technical depth and leadership skills, as you'll be guiding teams and influencing strategic direction.
Key aspects of the role include optimizing GPU server configurations, implementing high-throughput networking solutions, and ensuring reliable operation of production ML infrastructure. You'll collaborate with data scientists and ML engineers to understand and support their needs while maintaining system efficiency and reliability.
This hybrid position is based in Mountain View, CA, offering competitive compensation ($149,000-$247,000) and comprehensive benefits. The role requires 8+ years of experience in large-scale distributed systems, with significant focus on GPU-based ML workloads.
LinkedIn offers a culture built on trust, care, and inclusion, with opportunities for professional growth and impact. You'll be part of a team transforming how the world works through AI technology, while enjoying the stability and resources of a leading tech company.
Responsibilities For Sr Staff Engineer, ML Hardware Infrastructure and Performance
- Define and evaluate GPU server SKUs, memory configurations, and CPU-GPU ratios
- Design high-throughput, low-latency networking solutions for distributed training
- Lead end-to-end operational support for production GPU fleets
- Develop and maintain observability tooling
- Establish SLAs, SLOs, and best practices
- Partner with Data Scientists and ML Engineers to understand workload requirements
- Guide junior engineers in architecture best practices
- Influence long-term roadmap for ML/AI infrastructure
Requirements For Sr Staff Engineer, ML Hardware Infrastructure and Performance
- Bachelor's degree in Computer Science, Electrical Engineering, or related field or equivalent industry experience
- 8+ years of experience designing and managing large-scale, distributed systems or HPC environments
- 3+ years focused on GPU-based ML or AI workloads
- Deep knowledge of high-performance networking and parallel storage systems
- Experience with containerization and job schedulers
- Strong communication skills
- Masters degree preferred
Benefits For Sr Staff Engineer, ML Hardware Infrastructure and Performance
- Flexible work arrangements
- Comprehensive health benefits
- Professional development opportunities
- 401k benefits
