The opportunity
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
What you'll do
Research and prototype novel inference algorithms and model architectures: that exploit the unique capabilities of Cerebras hardware, with emphasis on speculative decoding, pruning/compression, sparse attention, and sparsity .
Design and evaluate new transformer architectures and variants for NLP and: computer vision to improve the trade-offs among quality, latency, throughput, and compute.
Identify and pursue high-impact research directions at the intersection of: model architecture, inference systems and hardware.
Train or adapt models where needed, and measure and analyze inference: performance using Cerebras tools to maximize throughput and minimize latency.
Collaborate with engineering and performance teams to understand practical: constraints and help translate successful research into usable systems, from inception through delivery.
Publish and present research findings and contribute to open-source releases where appropriate.
What they're looking for
- PhD in machine learning, computer science, or a closely related technical: field with 3+ years of relevant research or industry experience, OR a master’s degree with an exceptional record of original, independent research and 4+ years of relevant experience.
- Deep understanding of modern ML architectures and generative models in: language and/or vision domains, machine learning systems, and the principles that govern their inference behavior.
- Ability to reason about model and algorithm performance, particularly for inference workloads.
- Ability to identify practically relevant research questions, develop novel: methods, and evaluate their effects on model quality and inference efficiency.
