> Markdown version of [/jobs/ext/4548914-gpu-kernel-developer-fully-remote](https://www.wearedevelopers.com/jobs/ext/4548914-gpu-kernel-developer-fully-remote). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# GPU Kernel Developer - Fully Remote

- **Company:** Mercor, Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $145,600 - $187,200
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Profiling, Nvidia CUDA, Computer Programming, Linux Kernel, Machine Learning, Graphics Processing Unit (GPU), cuDNN, MLIR (Multi-Level Intermediate Representation)
- **Published:** November 3, 2026
- **Apply:** https://www.careerbuilder.com/job-details/gpu-kernel-developer-fully-remote-new-york-ny--c88cf5e6-d3da-436b-aa3b-25305d3fd4f3

## About the Role

GPU (Graphics Processing Unit), Kernel Programming, Artificial Intelligence (AI), Benchmarking, Research Laboratory, Kernel Programming, GPU (Graphics Processing Unit), Performance Modeling, Time Management, CUDA (Compute Unified Device Architecture), Must-Have

 * 3+ years of experience with CUDA, Triton, NKI, or Pallas (JAX).
 * Strong understanding of numerical-correctness criteria for kernels.
 * Experience with performance profiling and benchmarking (e.g., nsight, ncu).
 * Familiarity with common compilation and runtime failure modes.
 * Experience with at least three kernel task types.

Preferred

 * Experience with both NVIDIA GPU and custom-accelerator ecosystems.
 * Background in compiler engineering, MLIR, or intermediate-representation lowering.
 * Understanding of memory-hierarchy optimization.
 * Contributions to kernel libraries like cuBLAS, cuDNN, or Triton.

## Description

* Evaluate the quality and correctness of GPU/accelerator kernel development tasks for training and evaluating AI models.
 * Assess numerical correctness, performance-benchmarking fairness, and task scoping across diverse kernel task types.
 * Review compilation and runtime validity, providing clear, rubric-based written feedback.
 * Collaborate with AI research teams to improve model training and evaluation processes.
 * Work independently and asynchronously to meet deadlines and enhance AI model performance.