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Hiring: LLM Inference Performance Engineer (CUDA / Triton / GPU Kernel Optimization) Freelance Job Support Job Title: LLM Inference Performance Engineering & GPU Kernel Optimization Expert Job Type: Part time | Freelance | Remote Work Schedule: Monday to Friday 2 Hours/Day Job Description We are looking for an experienced LLM Inference Performance Engineering & GPU Kernel Optimization Expert to provide job support for a client. This is a remote freelance opportunity for professionals with strong expertise in optimizing Large Language Model (LLM) inference workloads. Required Skills - Deep expertise in CUDA programming - Strong hands-on experience with Triton - Advanced knowledge of NVIDIA GPUs and GPU architecture - Experience in LLM inference optimization - GPU Kernel Optimization and Performance Tuning - CUDA Kernel Development and Debugging - Memory Optimization, Profiling, and Latency Reduction - Experience with Transformer-based models and inference frameworks - Familiarity with PyTorch, TensorRT, vLLM, or similar inference frameworks is an added advantage - Excellent troubleshooting and problem-solving skills Responsibilities - Provide remote job support for ongoing client projects - Optimize GPU kernels and improve LLM inference performance - Analyze and resolve performance bottlenecks - Guide the client on CUDA, Triton, and NVIDIA GPU optimization techniques - Deliver high-quality technical support during scheduled sessions Job Details - Employment Type: Freelance / Part-Time - Mode: Remote - Support Hours: 2 Hours per day (Monday to Friday) - Experience Required: 5+ Years (Preferred) - Joining: Immediate If you have strong experience in CUDA, Triton, NVIDIA GPU optimization, and LLM inference performance engineering, we'd love to hear from you. Compensation: 25,000.00 - 30,000.00 per month Experience: - LLM InLLM & GPU Kernel Optimization: 5 years (Required) Work Location: Remote .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.