GPU Kernel Engineer – CUDA, Triton, Accelerator Performance

🔥 18 hours ago

🇦🇷 Argentina – Remote

💵 $65 / hour

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

👷🏻‍♀️ Engineer

👻 Ghost score 0%

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Anyone AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

📚 Education

🤖 Artificial Intelligence

🎯 Recruiter

💰 $1.5M Pre Seed Round - Anyone AI on 2022-06

Education • Artificial Intelligence • Recruitment

Anyone AI is a Latin America–focused career accelerator and training academy that develops software engineers into AI and machine learning professionals through intensive, remote, mentor-led programs. It combines technical coursework (e. g. , Machine Learning Developer track), professional preparation (resume/LinkedIn optimization, interview coaching, English practice), and a placement-oriented model (income-share payments and access to employer partnerships) to help graduates secure higher-paying AI roles globally. Anyone AI also operates a talent marketplace connecting its community to companies seeking AI talent.

📋 Description

• Review GPU and accelerator kernel implementations for correctness • Compare outputs against reference implementations • Evaluate numerical tolerance thresholds • Review kernel benchmarks and determine whether comparisons are fair • Identify performance bottlenecks and optimization opportunities • Assess whether performance targets are realistic given hardware limits • Review kernel translations and hardware migrations • Identify compilation, driver, memory, shape, and runtime issues • Determine whether technical tasks are genuinely difficult or incorrectly configured • Provide clear, actionable technical feedback • Implement and debug kernels • Optimize CUDA and Triton kernels • Translate between kernel frameworks • Perform hardware migration and operator fusion • Profile and benchmark performance • Verify numerical correctness • Debug compilation and runtime issues • Optimize memory hierarchy and kernel-level AI workload performance

🎯 Requirements

• 3+ years of hands-on experience developing, optimizing, or debugging GPU or accelerator kernels • Strong experience with at least two of: 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, and 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 • Experience writing kernels from technical specifications, translating kernels between frameworks, migrating kernels across hardware platforms, debugging incorrect implementations, optimizing kernel performance, and fusing multiple operations into optimized kernels • Experience across both NVIDIA GPU and custom accelerator ecosystems (nice to have) • Experience with AWS Trainium, TPU, JAX, or other accelerators (nice to have) • Compiler engineering experience (nice to have) • Familiarity with MLIR, XLA, or intermediate representation lowering (nice to have) • Contributions to GPU or ML kernel libraries (nice to have) • Experience with cuBLAS, cuDNN, Triton community kernels, or JAX/XLA custom calls (nice to have) • Experience with AI model evaluation, RLHF, or technical benchmark development (nice to have)

🏖️ Benefits

• $65 per hour compensation • Part-time, project-based consulting engagement • Remote work