ML Research Engineer (Inference)

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top 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. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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. About The Role As a Research Engineer on the Inference ML team at Cerebras Systems, you will adapt today's most advanced language and vision models to run efficiently on our flagship Cerebras architecture. You'll work alongside ML 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 Implement and adapt transformer-based models (NLP and/or vision) to run on Cerebras hardware Assist in optimizing models for inference performance (latency, throughput) Run experiments, analyze results, and support model improvements Help bring up and validate models on the Cerebras system Debug and troubleshoot model or system issues with guidance from senior team members Support profiling and performance analysis using internal tools Collaborate with cross-functional teams (ML, software, hardware) on model integration Minimum Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field 1–3 years of experience in software engineering or machine learning in a similar capacity (internships count) Experience with Python and at least one ML framework (e.g., PyTorch, Transformers, vLLM or SGLang) Understanding of deep learning concepts (e.g., neural networks, transformers) Experience with Generative AI and Machine Learning systems Strong programming skills in Python and/or C++ Preferred Qualifications Experience with speculative decoding, neural network pruning and compression, sparse attention, quantization, sparsity, post-training techniques, and inference-focused evaluations. Exposure to large language models or computer vision models Experience running experiments or tuning models Familiarity with tools like PyTorch, Hugging Face Transformers, or similar Basic understanding of performance concepts (e.g., latency, throughput) Experience working in Linux environments Why Join Cerebras People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: Build a breakthrough AI platform beyond the constraints of the GPU. Publish and open source their 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 that respects individual beliefs. Read our blog: Five Reaso