kernels Jobs

Anthropic

As a TPU Kernel Engineer, you will be responsible for identifying and addressing performance issues across various ML systems, including research, training, and inference. A significant part of this role involves designing and optimizing kernels specifically for the TPU. You will also provide crucial feedback to researchers regarding how model changes affect performance. The ideal candidate will have a proven history of solving large-scale systems problems and demonstrating expertise in low-level optimization.

$280k - $850k

Runpod is seeking a systems-focused Cloud Infrastructure Security Engineer to safeguard its bare-metal and virtualized GPU cloud infrastructure. This role is crucial for ensuring the security, multi-tenant isolation, and integrity of the underlying GPU cloud infrastructure. The ideal candidate will have deep knowledge of Linux systems, kernel internals, hypervisors, and containerization, focusing on preventing tenant breakouts, securing GPU hardware, and building resilient infrastructure for AI and machine learning workloads. You will operate with an attacker's mindset, identifying and patching vulnerabilities at the lowest levels of the stack.

$152k - $175k

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.

Cerebras Systems is seeking a hands-on SDET Technical Lead to establish and lead Release Integration Testing for AI Inference Core. This role is crucial for transforming integrated features into reliable production releases by defining the quality strategy across the entire release cycle, from feature integration to post-release learning. You will work across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware to identify and address release risks. This is a technical leadership position where you will design test architecture, lead debugging and release decisions, mentor engineers, and contribute to software and automation development.