"gpu kernels" Jobs
105 open tech roles matching “gpu kernels”, taken straight from company career pages — not reposted from other job boards. Most in demand right now: Python, CUDA, Kubernetes. Every listing is re-checked daily and closed roles are removed.
Showing 20 of 105 results
Join World Labs as a Performance Engineer to optimize AI models for speed and efficiency in a cutting-edge research environment.
Join Perplexity AI as an AI Inference Engineer to optimize and develop our inference engine for various model architectures.
Join Pika as a Senior/Staff ML Engineer to enhance AI-driven products through advanced inference acceleration and GPU optimization.
Design and operate large-scale GPU infrastructure for model inference and mid-training workloads at Reflection AI.
Join Fundamental as a Senior Applied Research Engineer to tackle technical challenges in AI model development for enterprise decision-making.
Join Baseten as a Software Engineer focusing on Model APIs to enhance AI model performance and developer experience.
Design and implement low-level systems software for GPU clusters in a pioneering AI infrastructure company.
Join Applied Intuition as an AI Performance Engineer to optimize large-scale machine learning workloads in a collaborative environment.
Join Fireworks AI as a Member of Technical Staff to design and build systems infrastructure for AI workloads at scale.
Join Fireworks AI as a Software Engineer focused on Performance Optimization to enhance AI infrastructure efficiency and speed.
Join CoreWeave as a Senior Systems Engineer to enhance the test framework for AI infrastructure at scale.
Join Kodiak Robotics as a Staff Machine Learning Engineer to design and deploy ML systems for autonomous trucking.
Join CoreWeave as a Senior Software Engineer to enhance network observability for GPU cloud services in a fast-growing company.
Join Inferact as a staff engineer to work on optimizing AI inference across the vLLM stack in a fully remote role.
Join Baseten as a Post-Training Research Engineer to build in-house tooling for efficient and high-quality machine learning models.