ML Research Scientist
Posted 2 Oct 2026
Advertised as “ML Research Scientist - Inference ML”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
Field of studyComputer Science, Machine learning (or related)
Study levelMaster's or higher
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.
This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
About The Role
As an ML Research Scientist on the Inference ML team at Cerebras Systems, you will invent and evaluate advanced algorithms, methods and model architectures for efficient inference on our flagship Cerebras architecture. You'll work alongside ML researchers and engineers to design, prototype, validate, and optimize methods and models, contributing to cutting-edge inference research on the world's fastest AI accelerator.
You will focus on pushing the frontier of speculative decoding, large-model pruning and compression, sparse attention, and sparsity-driven techniques to deliver low-latency, high-throughput inference at scale.
Hybrid role in Toronto, ON, CA or Sunnyvale, CA, USA.
Responsibilities
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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.
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Design and evaluate new transformer architectures and variants for NLP and computer vision to improve the trade-offs among quality, latency, throughput, and compute.
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Identify and pursue high-impact research directions at the intersection of model architecture, inference systems and hardware.
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Train or adapt models where needed, and measure and analyze inference performance using Cerebras tools to maximize throughput and minimize latency.
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Collaborate with engineering and performance teams to understand practical constraints and help translate successful research into usable systems, from inception through delivery.