Cerebras Systems

Staff Inference ML Runtime Engineer

Sunnyvale CA or Toronto Canada · Staff+

Sponsorship not specifiedDetected 97 days ago

PythonC++Machine LearningDeep LearningPyTorchLLMsComplianceCommunicationProblem Solving

About the role

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 play a key role in designing and implementing APIs, ML features, and tools that enable running state-of-the-art generative AI models on our custom hardware.
You will architect solutions that enable seamless model translation and execution, ensuring high throughput and low latency, while maintaining ease of use.

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.
Maintain our scalable serving backend for handling many concurrent requests per minute.
Optimize software to accelerate generative LLM inference by achieving high throughput and low latency.
Evaluate trade-offs between different approaches, clearly articulate design choices, and develop detailed proposals for implementing new features.
Build and maintain robust automated test suites to ensure software quality, performance, and reliability.
Lead cross-functional initiative across the company to deliver high-quality inference solutions.

Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Mathematics, or a related field.
8+ years of experience in large-scale software engineering, with a focus on deep learning or related domains.
Advanced proficiency in C++, with an emphasis on multi-threaded programming, performance optimization, and system-level development.
Demonstrated experience driving cross-functional projects.
Familiarity with LLM serving frameworks, such as vLLM, SGLang, and TensorRT-LLM.
Solid understanding of software architectural patterns for large-scale, high-performance applications.
Hands-on experience with ML frameworks, such as PyTorch, and a strong understanding of their underlying architectures.
Strong problem-solving skills, with the ability to balance technical depth with practical implementation constraints.

Drive and provide technical guidance to a team of software engineers working on complex machine learning integration projects.
Stay up-to-date with advancements in machine learning and deep learning, and apply state-of-the-art techniques to enhance our solutions.

This listing is sourced directly from Cerebras Systems's careers page and normalized into a canonical job model.