ML Runtime and Kernel Engineer - Core ML

Cerebras Systems•Sunnyvale, CA

About The Position

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. 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.

Requirements

Bachelor’s, Master’s, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
Experience developing high-performance systems software, ML systems, runtimes, compilers, or computational kernels.
Strong programming skills in C++ and Python.
Solid understanding of parallel programming, memory management, concurrency, data structures, and performance optimization.
Proven ability to debug and profile complex software across multiple layers of a system.
Familiarity with modern machine learning architectures and frameworks such as PyTorch or JAX.
Ability to work effectively with researchers and translate evolving algorithmic requirements into reliable software.

Nice To Haves

Experience with CUDA, Triton, low-level assembly, accelerator programming, or a C-like domain-specific language.
Experience with compiler internals, distributed runtimes, custom hardware interfaces, or HPC systems.
Understanding of machine learning fundamentals and ML systems, with the ability to reason about how algorithmic choices affect accuracy, systems implementation and performance.
Familiarity with LLM training or inference, including attention, KV-cache management, parallel generation, or distributed execution.
Experience developing software in an industrial or academic research environment where requirements evolve through experimentation.
Contributions to significant open-source systems, ML frameworks, compilers, or kernel libraries.

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.