ML Runtime and Kernel Engineer - Core ML

The role

This engineer will bridge the gap between research ideas and execution on Cerebras systems. You will work across ML frameworks, compilers, runtimes, and low-level kernels to implement algorithmic capabilities and optimize performance.

What you will do

• Design and implement runtime components and high-performance kernels.
• Translate research prototypes into efficient implementations for the Cerebras platform.
• Profile and debug performance across the ML framework, compiler, runtime, communication, and kernel layers.
• Optimize computation, memory movement, communication, and concurrency.
• Develop benchmarks, instrumentation, and automated tests.
• Collaborate with researchers and engineers to evaluate design alternatives.
• Contribute to software architecture and roadmap decisions.

Required qualifications

• 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 have

• 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.
• 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.

Benefits

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Job stability with startup vitality.
• Simple, non-corporate work culture that respects individual beliefs.

About the company

Cerebras Systems builds the world's largest AI chip. The company works with leading model labs, global enterprises, and cutting-edge AI-native startups.