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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Runtime and Kernel Engineer - **Company:** Cerebras Systems - **Location:** Sunnyvale, United States - **Contract:** Permanent contract - **Skills:** Automation of Tests, C++ (Programming Language), Compilers, Nvidia CUDA, Computer Programming, Computer Engineering, Data Structures, Software Debugging, Memory Management, Hardware Interface Design, Python (Programming Language), Linux Kernel, Machine Learning, Open Source Technology, Performance Tuning, Software Architecture, Tensorflow, Software Engineering, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Concurrency, Parallel Computation, Information Technology, Machine Learning Operations - **Published:** September 30, 2026 - **Apply:** https://startup.jobs/ml-runtime-and-kernel-engineer-core-ml-cerebras-ai-10220427 ## About the Role * 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. Preferred Skills & Qualifications * 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. ## Description 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. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Just-in-time Compilation in JVM](https://www.wearedevelopers.com/videos/240-just-in-time-compilation-in-jvm) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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