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# GPU Kernel Engineer - CUDA, Triton & Accelerator Performance

- **Company:** Anyone Ai
- **Location:** Madrid, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Software Code Optimization, Profiling, Nvidia CUDA, Software Debugging, Performance Tuning
- **Published:** September 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c81767550c9d0484

## About the Role

* 3+ years of hands-on experience developing, optimizing, or debugging GPU or accelerator kernels
 * Strong experience with at least two of the following:
 + CUDA
 + Triton
 + NKI / AWS Neuron
 + Pallas / JAX
 * Strong understanding of GPU performance optimization
 * Experience with kernel profiling tools such as Nsight, NCU, roofline analysis, or framework-native profilers
 * Understanding of:
 + Memory bandwidth
 + Compute throughput
 + GPU occupancy
 + Shared memory
 + Register pressure
 + Memory coalescing
 + Bank conflicts
 * Strong understanding of floating-point numerical correctness and tolerance thresholds
 * Experience debugging kernel compilation and runtime issues
 * Ability to distinguish software defects, environment problems, and genuine optimization challenges

Relevant Experience

Candidates should have experience with several of the following types of work:

 * Writing kernels from technical specifications
 * Translating kernels between CUDA, Triton, or other frameworks
 * Migrating kernels across hardware platforms
 * Debugging incorrect kernel implementations
 * Optimizing kernel performance
 * Fusing multiple operations into optimized kernels

Nice to Have

 * Experience across both NVIDIA GPU and custom accelerator ecosystems
 * Experience with AWS Trainium, TPU, JAX, or other accelerators
 * Compiler engineering experience
 * Familiarity with MLIR, XLA, or intermediate representation lowering
 * Contributions to GPU or ML kernel libraries
 * Experience with cuBLAS, cuDNN, Triton community kernels, or JAX/XLA custom calls
 * Experience with AI model evaluation, RLHF, or technical benchmark development

## Description

We're looking for engineers with hands-on experience writing and optimizing kernels across frameworks such as CUDA, Triton, NKI, or Pallas, with a strong understanding of numerical correctness, GPU performance, memory optimization, and benchmarking.

What You'll Work On

You'll work with GPU and accelerator kernel tasks involving:

 * Kernel implementation and debugging
 * CUDA and Triton optimization
 * Translation between kernel frameworks
 * Hardware migration
 * Operator fusion
 * Performance profiling and benchmarking
 * Numerical correctness verification
 * Compilation and runtime debugging
 * Memory hierarchy optimization
 * Kernel-level AI workload performance

You'll assess whether implementations are technically correct, efficiently designed, reproducible, and appropriately optimized for the target hardware., * Reviewing GPU and accelerator kernel implementations for correctness

 * Comparing outputs against reference implementations
 * Evaluating numerical tolerance thresholds
 * Reviewing kernel benchmarks and determining whether comparisons are fair
 * Identifying performance bottlenecks and optimization opportunities
 * Assessing whether performance targets are realistic given hardware limits
 * Reviewing kernel translations and hardware migrations
 * Identifying compilation, driver, memory, shape, and runtime issues
 * Determining whether technical tasks are genuinely difficult or incorrectly configured
 * Providing clear, actionable technical feedback


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