ML Runtime and Kernel Engineer - Core ML
Sunnyvale, CA • FullTime
Posted 19d ago
About the job
The Core ML team is seeking an engineer to bridge the gap between novel machine learning research 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 transform 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. This role is crucial for advancing the capabilities of Cerebras' unique AI chip architecture.
Responsibilities
- Design and implement runtime components and high-performance kernels for novel Core ML algorithms.
- Translate research prototypes into efficient implementations for the Cerebras platform.
- Profile and debug performance across 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 for functionality, performance, and numerical correctness.
- Collaborate with researchers and engineers to evaluate design alternatives and deliver end-to-end capabilities.
- Contribute to software architecture and roadmap decisions by identifying limitations and improvements.
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 system layers.
- Familiarity with modern machine learning architectures and frameworks like PyTorch or JAX.
- Ability to work effectively with researchers and translate evolving algorithmic requirements into reliable software.
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.