AI Performance Engineer – GPU & ROCm

Job Summary We are looking for a GPU AI Performance Engineer to optimize and deploy AI and machine-learning workloads using ROCm. You will improve the speed, memory efficiency, and scalability of AI training and inference workloads. You will also work with GPU software, AI frameworks, profiling tools, and cloud or containerized environments. Key Responsibilities Optimize AI models for GPU performance. Improve training speed, inference latency, memory usage, and multi-GPU scalability. Work with large language models, vision models, multimodal AI, and generative AI. Develop and troubleshoot applications using ROCm and HIP. Work with PyTorch, TensorFlow, ONNX Runtime, vLLM, SGLang, or MIGraphX. Profile GPU applications and identify performance bottlenecks. Apply mixed precision, quantization, kernel tuning, operator fusion, and memory optimization. Support AI deployment on Linux, cloud, edge, and containerized platforms. Develop performance benchmarks and automated validation tests. Collaborate with hardware, compiler, runtime, framework, and infrastructure teams. Required Qualifications Bachelor’s or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field. Strong programming skills in C++ and Python. Experience with Linux and GPU computing. Hands-on experience with ROCm, HIP, CUDA, or similar GPU technologies. Understanding of GPU architecture, parallel programming, and memory management. Experience with PyTorch, TensorFlow, ONNX Runtime, or similar AI frameworks. Experience profiling and optimizing GPU applications. Knowledge of AI model training, inference, and performance optimization. Preferred Qualifications Experience optimizing large language models or generative AI applications. Experience with vLLM, SGLang, or distributed inference. Familiarity with LLVM, MLIR, or compiler technologies. Experience migrating workloads between CUDA and HIP. Knowledge of Kubernetes, containers, and distributed AI systems. Contributions to ROCm or other open-source GPU projects.

Optimize and deploy AI and machine learning workloads using ROCm to improve speed, memory efficiency, and scalability. Profile GPU applications to identify bottlenecks and develop performance benchmarks for various AI models.

Requires a degree in Computer Science or a related field with strong proficiency in C++, Python, and GPU computing. Candidates must have hands-on experience with ROCm, HIP, or CUDA and familiarity with major AI frameworks.

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

• ROCm
• HIP
• GPU Optimization
• C++
• Python
• PyTorch
• TensorFlow
• ONNX Runtime
• vLLM
• SGLang
• MIGraphX
• CUDA
• Parallel Programming
• Mixed Precision
• Quantization
• Kernel Tuning

• Engineering
• Technology
• Software
• Data & Analytics
• Manufacturing

Formation minimale

Baccalauréat

Expérience minimale

5+ ans

Postuler avant le

29 août 2026

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Niveau d’expérience

Mid-Senior level

Mode de candidature

La candidature directe est offerte