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 a Senior Research Engineer on the Inference ML team atCerebrasSystems, you will adapt today's most advanced language and vision models to run efficiently on our flagshipCerebrasarchitecture.You'llwork alongside ML researchers and engineers to design, prototype,validate, andoptimizemodels, gaining end-to-end exposure tocutting-edgeinference 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.

Responsibilities

- Design, implement, andoptimizestate-of-the-arttransformer architectures for NLP and computer vision onCerebrashardware.

- Research and prototype novel inference algorithms and model architectures that exploit the unique capabilities ofCerebrashardware, with emphasis on speculative decoding,



pruning/compression, sparse attention, and sparsity .

- Train models to convergence, perform hyperparameter sweeps, and analyze results to inform next steps.

- Bring up new models on theCerebrassystem,validatefunctional correctness, and troubleshoot any integration issues.

- Profile and optimize model code usingCerebrastools to maximize throughput and minimize latency.

- Develop diagnostic tooling or scripts to surface performance bottlenecks and guide optimization strategies for inference workloads.

- Collaborate across teams, including software, hardware, andproduct, to drive projects frominceptionthrough delivery.

Minimum Qualifications

- One of the following education and experience combinations:

- Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related technical field AND 7+ years of ML software development experience, OR

- Master’s degree in Computer Scienceor related technical field AND 4+ years of software development experience, OR

- PhD in Computer Science or related technical field with 2+ years of relevant research or industry experience, OR

- Equivalent practical experience.

- 4+ years of experience testing,maintaining,



or launching software products, including 2+ years of experience with software design and architecture.

- 3+ years of experience in software development focused on machine learning (e.g., deep learning, large language models, or computer vision).

- Robust programming skills in Python and/or C++.

- Experience with Generative AI and Machine Learning systems.

- Evidence of research impact in machine learning, such as publications at top conferences (NeurIPS, ICLR, ICML, ACL, EMNLP, MLSys) or comparable contributions to widely used open-source projects or high-quality preprints.

Preferred Qualifications

- Master’s degree or PhD in Computer Science, Computer Engineering, or a related technical field.

- Experience independently driving complex ML or inference projects from prototype to production-quality implementations.

- Hands-on experience with relevant ML frameworks such as PyTorch, Transformers,vLLM, orSGLang .

- Experience with large language models, mixture-of-experts models, multimodal learning, or AI agents.

- Experience with speculative decoding , neural network pruning and compression , sparse attention , quantization , sparsity , post-training techniques, and inference-focused evaluations.

- Familiarity with large-scale model training and deployment, including performance and cost trade-offs in production systems.

- Triton/CUDA experience is a big plus.

Required Skills & Attributes

- Proficiencywith at least one major ML framework ( PyTorch, Transforme

📌 Senior Research Engineer (Canada)
🏢 Cerebras Systems
📍 Canada