ML Runtime and Kernel Engineer

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

The Core ML team develops novel machine learning algorithms that take advantage of the unique capabilities of the Cerebras Wafer-Scale Engine. Our work spans efficient LLM training and inference, parallel and diffusion-based generation, sparsity, scaling laws, and training dynamics.

We are looking for an engineer to bridge the gap between promising research ideas and efficient execution on Cerebras systems. You will work across ML frameworks, compilers, runtimes, and low-level kernels to implement new algorithmic capabilities, diagnose performance bottlenecks, and turn research prototypes into robust, high-performance demonstrations.

Depending on your background, your work may emphasize runtime capabilities such as token orchestration, scheduling, communication, and distributed execution; low-level kernel development for novel ML operations; or a combination of both.

Responsibilities

• Design and implement runtime components and high-performance kernels required by novel Core ML algorithms.

• Translate research prototypes into efficient implementations for the Cerebras platform, including reference implementations and comparisons on GPUs where useful.

• Profile and debug performance across the ML framework, compiler, runtime, communication, and kernel layers.

• Optimize computation, memory movement, communication, and concurrency for large-scale training and low-latency inference.

• Develop benchmarks, instrumentation, and automated tests that validate functionality, performance, and numerical correctness.

• Collaborate closely with Core ML researchers and compiler, runtime, kernel, and inference engineers to evaluate design alternatives and deliver end-to-end capabilities.

• Contribute to software architecture and roadmap decisions by identifying recurring limitations and high-leverage platform improvements.