Embedded Hardware Engineer (Remote | $80 –$100/hr)

CUDA Engineering Expert Position: CUDA Engineering Expert Type: Hourly Contract Compensation: $80–$100/hour Location: Remote About the Opportunity This opportunity is for experienced GPU Performance Engineers, CUDA Developers, and GPU Kernel Optimization Specialists interested in contributing to advanced AI research and evaluation projects. The role focuses on analyzing, optimizing, and evaluating GPU kernels across modern hardware architectures. You'll leverage your expertise in CUDA, C++17, GPU profiling, and performance optimization to improve computational efficiency and help advance next-generation AI systems. This is a contract-based opportunity for professionals passionate about maximizing GPU performance and hardware utilization. Responsibilities Analyze and optimize GPU kernels for performance, efficiency, scalability, and hardware utilization. Use profiler metrics such as L2 cache hit rate, occupancy, memory throughput, warp efficiency, and related performance indicators to guide optimization decisions. Identify bottlenecks in GPU kernel implementations and recommend performance improvements. Develop, review, and optimize C++17, Python, and GPU programming code. Apply expertise in CUDA, HIP, shader programming, or related GPU programming frameworks to improve kernel performance. Document optimization methodologies, profiling results, and engineering decisions with clear technical reasoning. Collaborate with engineering teams to evaluate and improve AI-related GPU workloads. Required Qualifications Availability to work at least 20 hours per week. Strong proficiency in C++ (through C++17). Working knowledge of Python and Git. Professional experience with at least one GPU programming framework, including CUDA, HIP, Slang, HLSL, GLSL, or similar technologies. 1+ year of professional or graduate-level research experience working with GPU programming or optimization. Strong understanding of GPU architecture and performance profiling techniques. Experience using profiler metrics to optimize GPU kernels efficiently. Strong analytical and problem-solving skills with excellent attention to performance optimization. Preferred Qualifications Experience with CUDA C++ Core Libraries, inline PTX assembly, or Tensor Core optimization. Experience optimizing kernels for NVIDIA Blackwell or other modern GPU architectures. Familiarity with NVIDIA Nsight Compute or similar GPU profiling tools. Experience working with GPU platforms from NVIDIA, AMD, Qualcomm, or related hardware vendors. Contributions to open-source GPU optimization or high-performance computing projects. Experience supporting AI, machine learning, or high-performance computing workloads. Compensation Competitive compensation of $80–$100/hour. Weekly payments. Independent contractor engagement. Application Process Easy Apply on LinkedIn Check Email for Next Steps Participate in Resume Evaluation & Interview Stage

Analyze and optimize GPU kernels for performance, efficiency, and scalability across modern hardware architectures. Develop and review C++17 and Python code while documenting optimization methodologies and profiling results.

Requires strong proficiency in C++17, Python, and at least one GPU programming framework like CUDA or HIP. Candidates need 1+ year of professional or graduate-level experience in GPU programming and performance profiling.

• GitSouhaitée
• PythonSouhaitée

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• CUDA
• C++17
• GPU Profiling
• Python
• GPU Kernel Optimization
• HIP
• Shader Programming
• Git
• NVIDIA Nsight Compute
• PTX Assembly
• Tensor Core Optimization
• GPU Architecture

• Engineering
• Software
• Technology
• Science & Research
• Manufacturing

Formation minimale

Maîtrise

Expérience minimale

2+ ans

Langue de l’offre

anglais

Heures de travail

20 heures par semaine

Niveau d’expérience

Mid-Senior level

Mode de candidature

La candidature directe est offerte