CUDA / GPU Performance Engineer – Kernel Optimization

🔥 0 minutes ago

🌐 India, Romania, +4 more countries – Remote

🇮🇳 India – Remote 🇷🇴 Romania – Remote 🇧🇷 Brazil – Remote 🇵🇰 Pakistan – Remote 🇵🇱 Poland – Remote 🇦🇷 Argentina – Remote

💵 $60 - $100 / hour

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

👷🏻‍♀️ Engineer

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Gramian Consulting

WebsiteLinkedInAll Job Openings

2 - 10 employees

Founded 2025

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

Gramian Consulting is a remote-first consulting firm that connects engineering and data/AI talent with organizations through talent augmentation, recruiting, dedicated teams, and contractor management. The firm provides Data & AI services including LLM training and fine-tuning, AI agents and assistants, MLOps, and AI infrastructure, and it offers mentorship and education programs for career readiness, interview preparation, and international market orientation. Rooted in hands-on engineering and recruiting experience, Gramian helps clients scale technical teams and extract business value from AI while developing individual talent.

📋 Description

• Analyze and optimize CUDA kernels for throughput, latency, and hardware utilization. • Profile GPU workloads to identify compute, memory, synchronization, and execution bottlenecks. • Develop and implement targeted kernel optimization strategies. • Refactor C++ and CUDA codebases for performance, maintainability, and portability. • Evaluate kernel behavior across different GPU architectures and hardware generations. • Develop or adapt shader and compute workflows using GLSL and WebGPU. • Use GPU profiling tools to validate improvements and compare performance. • Document optimization approaches, benchmarks, findings, and performance gains. • Contribute technical input to GPU architecture and performance-design discussions. • Evaluate emerging GPU programming techniques and apply relevant improvements.

🎯 Requirements

• Strong professional experience with CUDA programming and GPU kernel optimization. • Advanced proficiency in C++, ideally in high-performance or systems programming environments. • Proven experience profiling and tuning GPU workloads for performance. • Hands-on experience with GPU profiling tools such as NVIDIA Nsight or comparable tools. • Strong understanding of GPU architecture, memory hierarchy, parallel execution, and synchronization. • Experience analyzing performance across different GPU hardware generations. • Hands-on experience with GLSL and/or WebGPU for shader or compute development. • Ability to document performance findings and technical decisions clearly in English.