ashby
Staff Inference ML Runtime Engineer
Cerebras Systems
USA
Full-time
Senior · 8+ years experience
Onsite
Discovered 3 weeks ago
PythonC++vLLMSGLangTensorRT-LLMPyTorch
Free
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Role Overview
The Inference ML Engineering team at Cerebras Systems is dedicated to enabling our fast generative inference solution through simple APIs powered by a distributed runtime that runs on large clusters of our own hardware. Our mission is to empower enterprises, developers, and researchers to unlock the full potential of our platform, leveraging its performance, scalability, and flexibility.
As a Senior Software Engineer on the Inference ML Engineering team, you will design and implement APIs, machine learning features, and tools that enable state-of-the-art generative AI models to run efficiently on our custom hardware. You will collaborate with cross-functional teams to build scalable, high-performance inference solutions while helping shape the evolution of our ML ecosystem.
Key Skills for This Role
PythonC++vLLMSGLangTensorRT-LLMPyTorch
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About the Role
The Inference ML Engineering team at Cerebras Systems is dedicated to enabling our fast generative inference solution through simple APIs powered by a distributed runtime that runs on large clusters of our own hardware. Our mission is to empower enterprises, developers, and researchers to unlock the full potential of our platform, leveraging its performance, scalability, and flexibility.
As a Senior Software Engineer on the Inference ML Engineering team, you will design and implement APIs, machine learning features, and tools that enable state-of-the-art generative AI models to run efficiently on our custom hardware. You will collaborate with cross-functional teams to build scalable, high-performance inference solutions while helping shape the evolution of our ML ecosystem.
Responsibilities
- Drive and provide technical guidance to a team of software engineers working on complex machine learning integration projects.
- Design and implement ML features (e.g., structured outputs, biased sampling, predicted outputs) that improve performance of generative AI models at inference time.
- Design and implement high-throughput, low-latency multimodal inference models that support delivery of image, audio, and video inputs and outputs.
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