Senior Research Engineer

Cerebras Systems builds the world’s largest AI chip, 56 times larger than GPUs. Our novel wafer‑scale architecture delivers industry‑leading training and inference speeds while providing the simplicity of a single device. This allows machine‑learning users to run large‑scale models without managing large clusters of GPUs or TPUs. About the Role As a Senior Research Engineer on the Inference ML team, you will adapt today’s most advanced language and vision models to run efficiently on our flagship Cerebras architecture. You’ll work alongside researchers and engineers to design, prototype, validate, and optimize models, gaining end‑to‑end exposure 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. Responsibilities Design, implement, and optimize state‑of‑the‑art transformer architectures for NLP and computer vision on Cerebras hardware. 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. Train models to convergence, perform hyper‑parameter sweeps, and analyze results to inform next steps. Bring up new models on the Cerebras system, validate functional correctness, and troubleshoot integration issues. Profile and optimize model code using Cerebras tools 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, and product, to drive projects from inception through 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 field AND 7+ years of ML software development experience; Master’s degree in Computer Science or related field AND 4+ years of software development experience; PhD in Computer Science or related field with 2+ years of relevant research or industry experience; 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). Strong programming skills in Python and/or C++. Experience with Generative AI and Machine Learning systems. Preferred Qualifications Master’s degree or PhD in Computer Science, Computer Engineering, or a related 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, or SGLang. 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 Proficiency with at least one major ML framework (PyTorch, Transformers, vLLM, or SGLang). Deep understanding of transformer‑based models in language and/or vision domains, with demonstrated experience implementing and optimizing them. Proven ability to implement custom layers, operators, and backpropagation logic. Strong foundation in performance optimization on specialized hardware (e.g., GPUs, TPUs, or HPC interconnects). Deep understanding of modern ML architectures and strong intuition for optimizing their performance, particularly for inference workloads using sparse attention, pruning/compression, and speculative decoding. Track record of owning problems end‑to‑end and autonomously acquiring whatever knowledge is needed to deliver results. Self‑directed mindset with a demonstrated ability to identify and tackle the most impactful problems. Collaborative approach with humility, eagerness to help colleagues, and commitment to team success. Genuine passion for AI and a drive to push the limits of inference performance. Hybrid role in Toronto, ON, CA or Sunnyvale, CA, USA. Why Join Cerebras 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. Enjoy job stability with startup vitality. Our simple, non‑corporate work culture respects individual beliefs. Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth, and support of those around them. This website or its third‑party tools process personal data. For more details, click here to review our CCPA disclosure notice. #J-18808-Ljbffr