ML Runtime and Kernel Engineer - Core ML

We Love AI Jobs; Sunnyvale, Northern (CA, KY); Hybrid; USD 210,000 - 270,000; Full time

Job summary

Cerebras Systems in Sunnyvale, CA, is seeking a Runtime and Kernel Engineer to design and implement high-performance runtime components and kernels for Core ML algorithms on the Cerebras platform.

You will profile, optimize, and debug across ML frameworks, compilers, runtimes, and kernel layers, and translate research prototypes into robust demonstrations. Strong C++, Python, and GPU programming skills are essential for this hybrid role.

Qualifications

• Experience developing high-performance systems software and ML systems.
• Strong programming in C++ and Python with debugging and profiling skills.
• Familiarity with PyTorch or JAX and ML framework concepts.
• Ability to translate research ideas into robust software implementations.

Responsibilities

• Design and implement runtime components and high-performance kernels for Core ML algorithms.
• Profile and debug performance across ML frameworks, compilers, runtimes, and kernels.
• Build benchmarks, instrumentation, and automated tests for functionality and accuracy.
• Collaborate with Core ML researchers and engineers to evaluate design options and deliver end-to-end capabilities.

Skills

C++

Python

High-performance kernels

Runtime and compiler systems

Parallel programming

Performance profiling and optimization

PyTorch

JAX

CUDA

Education

Bachelor’s, Master’s, PhD, or equivalent experience

Tools

Triton

CUDA

Job description

# ML Runtime and Kernel Engineer - Core MLCerebrasSunnyvale, CA (Hybrid)Full-timeAI tools:PyTorchJAXCUDATritonUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageThe engineer builds runtime components and high-performance kernels that bring Core ML research ideas to Cerebras systems. They work across frameworks, compilers, runtimes, and kernels to improve training and inference performance and validate new capabilities.* Permanent* Hybrid* Bachelor's, Master's, PhD, or equivalent experience### Skills and expertiseC++PythonHigh-performance kernelsRuntime and compiler systemsParallel programmingPerformance profiling and optimizationPyTorchCUDA### Key responsibilities* Implement runtime components and high-performance kernels for novel machine learning algorithms.* Profile and debug performance across frameworks, compilers, runtimes, communication, and kernels.* Build benchmarks, instrumentation, and tests for functionality, performance, and numerical correctness.## How this job comparesCompared with 329 open mid-level machine learning jobs on October 10, 2026.Pay: This job does not publish its pay. The 119 open mid-level machine learning jobs that publish US pay start at $180K a year on average.Similar open jobs: We Love AI Jobs lists 329 open mid-level machine learning jobs at 145 companies.Remote work: This job is hybrid. 66 of the 329 mid-level machine learning jobs (20%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. The Core ML team develops machine learning algorithms, and this role implements and optimizes them for training and inference. The posting specifically calls for work with ML frameworks, kernels, and LLM workloads.## Full descriptionCerebras 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 RoleThe 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.