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Cerebras develops wafer-scale AI hardware and integrated software for high-performance model training and inference. Its Wafer-Scale Engine powers the CS-3 system and Cerebras Inference Cloud, which supports OpenAI API-compatible workflows and deployment across on-premise, cloud, and edge environments. The company’s platform is designed for applications including model serving, fine-tuning, pre-training, real-time AI agents, genomics, drug discovery, cybersecurity, and enterprise search. Cerebras brings together specialized chips, systems, and software for organizations building and operating demanding artificial intelligence workloads, with a team of 501–1,000 employees.
ML Runtime and Kernel Engineer, Core ML
Develop high-performance runtimes and kernels that translate Core ML research into production software. Build and optimize software for Cerebras’ wafer-scale AI platform and machine learning systems.
Description
- Design and implement runtime components and high-performance kernels for new Core ML algorithms
- Convert research prototypes into efficient Cerebras platform implementations, including reference versions and GPU comparisons where useful
- Profile and troubleshoot performance across ML frameworks, compilers, runtimes, communication systems, and kernels
- Optimize computation, memory transfers, communication, and concurrency for large-scale training and low-latency inference
- Create benchmarks, instrumentation, and automated tests for functionality, performance, and numerical accuracy
- Work closely with Core ML researchers and compiler, runtime, kernel, and inference engineers
- Inform software architecture and roadmap decisions by identifying recurring constraints and opportunities to improve the platform
Requirements
- Bachelor’s, master’s, PhD, or equivalent practical experience in computer science, computer engineering, electrical engineering, or a related discipline
- Experience building high-performance systems software, machine learning systems, runtimes, compilers, or computational kernels
- Strong C++ and Python programming skills
- Sound knowledge of parallel programming, memory management, concurrency, data structures, and performance optimization
- Demonstrated ability to debug and profile complex software across multiple system layers
- Familiarity with modern machine learning architectures and frameworks such as PyTorch or JAX
- Ability to collaborate effectively with researchers and turn evolving algorithmic needs into dependable software
- Preferred: experience with CUDA, Triton, low-level assembly, accelerator programming, or a C-like domain-specific language
- Preferred: experience with compiler internals, distributed runtimes, custom hardware interfaces, or high-performance computing systems
- Preferred: understanding of machine learning fundamentals and ML systems
- Preferred: familiarity with LLM training or inference, including attention, KV-cache management, parallel generation, or distributed execution
- Preferred: experience developing software in an industrial or academic research setting
- Preferred: contributions to substantial open-source systems, machine learning frameworks, compilers, or kernel libraries
Benefits
- Help build an AI platform designed beyond traditional GPU constraints
- Opportunities to publish and open-source advanced AI research
- Work with one of the world’s fastest AI supercomputers
- Startup energy combined with job stability
- A straightforward, non-corporate culture that respects individual beliefs
- An equal and diverse workplace
- Ongoing opportunities for learning, development, and support
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