Anyone Ai

GPU Kernel Engineer – CUDA, Triton & Accelerator Performance

Buenos Aires, Distrito Federal, Argentina · Remote (country restricted) · Contract

USD 65–65/hour

Apply on the employer’s site ↗See how well you match this job

You apply directly with the employer. ROLIVA does not submit applications from this page and is not the employer.

About the role

Description from the employer’s posting. Check the employer’s page for the current version.

EmployerAnyone Ai
LocationBuenos Aires, Distrito Federal, Argentina
Work styleRemote (country restricted)
Employment typeContract
Employer posted26 days ago
Salary (as stated by the employer)USD 65 – 65 per hour

Anyone AI is recruiting experienced GPU Kernel Engineers for a specialized project focused on reviewing, debugging, and evaluating high-performance compute kernels used in AI workloads.

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

Show full description

You’ll assess whether implementations are technically correct, efficiently designed, reproducible, and appropriately optimized for the target hardware.

What We’re Looking For

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

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

What You’ll Be Responsible For

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

Engagement

Work Type: Remote
Engagement: Part-time, project-based consulting
Focus: GPU kernels, performance engineering, debugging, and technical evaluation

This role is ideal for engineers who enjoy working close to the hardware, optimizing GPU workloads, debugging low-level performance issues, and pushing AI compute systems toward their performance limits.

See how well you match this job Apply on the employer’s site ↗

Check your resume against this job Free, no sign-up. Nothing you paste is stored.

Staying safe while you apply

Never pay to apply, interview or accept an offer. ROLIVA never asks you for payment to apply to a job.
Apply only through the official “Apply on the employer’s site” button, on the employer’s own site, and check that a recruiter’s email uses that employer’s own domain rather than a free email address.
Be cautious of an offer made with no real interview, or pressure to decide quickly or share financial details early.

Does something look wrong with this listing? Use “Report broken link” in the role details, or read ROLIVA’s full job-safety guidance ↗.

Preparing for this role with ROLIVA

Check the fit.
Compare the requirements with the experience you have confirmed, including gaps.
Prepare honestly.
Build interview stories and application drafts from your own evidence.
Keep track.
Save the role and record your own application status and follow-ups.

Create a free workspace or log in.