ML Research Engineer (Inference)
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 Research Engineeron 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 ofspeculative decoding,large-model pruning and compression,sparse attention, andsparsity-driventechniques 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 withspeculative 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.
Find out more about what it's like to work at Cerebras here!
Apply today and become part of the forefront of groundbreaking advancements in AI!
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.
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