ML Research Engineer (Inference)

Confirmed live in the last 24 hours

Cerebras Systems

Bengaluru, Karnataka, India

On-site

Posted April 8, 2026

Job Description

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 integratio

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