ML Research Scientist - Inference ML

The role

You will invent and evaluate advanced algorithms, methods and model architectures for efficient inference on Cerebras architecture. You will focus on speculative decoding, large-model pruning and compression, sparse attention, and sparsity-driven techniques.

What you will do

• Research and prototype novel inference algorithms and model architectures that exploit Cerebras hardware.
• Design and evaluate new transformer architectures and variants for NLP and computer vision.
• Identify and pursue high-impact research directions at the intersection of model architecture, inference systems and hardware.
• Train or adapt models and measure and analyze inference performance using Cerebras tools.
• Collaborate with engineering and performance teams to translate research into usable systems.
• Publish and present research findings and contribute to open-source releases.

Required qualifications

• 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,
• 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.
• Evidence of research impact in machine learning, statistics, information theory, or a closely related field, through publications in leading conferences or journals, high-quality preprints, or widely
• Strong Python programming skills and proficiency with relevant ML frameworks such as PyTorch, Transformers, vLLM, or SGLang.
• Self-directed mindset with a demonstrated ability to identify and tackle the most impactful problems.
• Collaborative and generous with colleagues, with the ability to communicate ideas clearly across research and engineering teams.
• Genuine passion for AI and a drive to push the limits of inference performance.

Nice to have

• Original research in one or more of: speculative decoding, neural network pruning and compression, sparse attention, quantization, mixture-of-experts models, sparsity, post-training techniques, or inf
• Track record of owning complex ML or inference projects end-to-end.
• Experience designing novel model architectures.
• Strong foundation in performance optimization on specialized hardware (e.g., GPUs, TPUs, or HPC interconnects).
• Familiarity with large-scale model training and deployment, including performance and cost trade-offs in production systems.
• Experience with Triton, CUDA, or C++

Benefits

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Job stability with startup vitality.
• Simple, non-corporate work culture that respects individual beliefs.

About the company

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. The company works with leading model labs, global enterprises, and cutting-edge AI-native startups.