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