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Added 1 month ago
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Cerebras Systems

ML Runtime and Kernel Engineer - Core ML

Toronto, ON, Canada

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Job details

Posting language: English
Posted: Sep 28, 2026

Original job description

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.

About The Role

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.

Skills & 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.

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.

Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

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

Find out more about what it's like to work at Cerebras here!

Apply today and become part of the forefront of groundbreaking advancements in AI!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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More about this job

Responsibilities

The ML Runtime and Kernel Engineer at Cerebras Systems will build runtime pieces and fast low-level code for new machine learning methods. They will also find and fix slow spots across the software, and work closely with researchers and other engineers.

Requirements

Cerebras Systems needs candidates with a background in computer science, computer engineering or electrical engineering, plus experience building high-performance systems, ML systems, compilers or kernels. Strong C++ and Python skills, and familiarity with PyTorch or JAX, are required.

Skills

C++
Python
PyTorch
JAX
CUDA
Triton
Low-level assembly
Accelerator programming
Domain-specific language
Compiler internals
Distributed runtimes
Parallel programming
Memory management
Concurrency
Performance optimization
Profiling and debugging
Benchmarking
Numerical correctness testing
LLM inference
LLM training
Attention mechanisms
KV-cache management
Distributed execution
Open-source contribution
Machine learning fundamentals

Keywords

C++
Python
PyTorch
JAX
CUDA
Triton
ML runtime
kernel engineering
compilers
parallel programming
LLM inference
LLM training
KV-cache
distributed training
high-performance computing
performance profiling
benchmarking
machine learning systems
wafer-scale engine
AI accelerator

Department

software_development

Tools

C++
Python
PyTorch
JAX
CUDA
Triton

Languages

English

Fields of study

Computer Science
Computer Engineering
Electrical Engineering

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Jobs near Toronto, ON