ML Algorithm Mapping and Performance Engineer, Core ML
Cerebras · Sunnyvale, CA
CHECKED 8H AGO
Infrastructureremote
At a glance
Highlights
- Map novel efficient training and inference algorithms to the world's largest AI chip.
- Build Pareto frontiers trading off quality, latency, throughput, memory and compute.
- Directly influence future Cerebras hardware and software co-design directions.
Ideal candidate
Systems-minded ML thinker who loves modeling trade-offs from kernels to end-to-end performance and turning analysis into clear research and hardware direction.
Requirements
- Bachelor's, Master's, PhD or equivalent in Computer Science, Computer Engineering, Electrical Engineering, Mathematics or related field
- Strong foundation in computer architecture, parallel computing, and systems performance
- Strong understanding of machine learning fundamentals and ML systems, including model and algorithmic choices
Lab profile
About Cerebras
Builds wafer-scale AI chips and cloud systems for ultra-fast training and inference.
What they build
- Wafer-scale AI chips
- AI training systems
- Cloud inference service
Known for
- 56x larger chips than GPUs
- 10x faster inference than GPU clouds
- Industry-leading training speed