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Ai/ml Performance Engineer - Gpu Optimization

Finland, Helsinki · Job Posted April 16, 2026
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Job Description

As an AI Performance Engineers you will focus on pushing machine learning workloads to peak hardware efficiency. The emphasis of this call is on analysis, profiling, debugging and optimization at application/workload-level; however a broad understanding of low-level GPU execution and kernel optimization is a major advantage.

Job Responsibility

  • Explore and benchmark ML models and workloads (including diffusion models, LLMs, and multimodal systems) to identify bottlenecks across compute, memory, and networking layers
  • Optimize performance for inference and training on AMD GPUs, including parallelization strategies, quantization techniques, serving orchestration, network communication and distributed execution
  • Perform deep profiling to uncover inefficiencies in ML frameworks, data pipelines, compiler tools, and key tensor operations such GEMMs, Convs and Attention, to name a few
  • Support AMD top-tier customers to improve model throughput, reduce latency, and optimize resource utilization across multi-GPU and cluster environments
  • Work closely with hardware, compiler, and software teams to drive improvements across the full ROCm stack
  • Communicate performance bottlenecks, solutions, and optimization strategies to stakeholders
  • Work with international teams located across Europe, US and Asia

Requirements

  • Experience with profiling, debugging, benchmarking, and optimization tools
  • Familiarity with ML frameworks (e.g., PyTorch, JAX, TF) and inference serving frameworks (e.g., vLLM, SGLang)
  • Strong C++ and/or Python skills, along the basics: unix, git, terminal, debugging, testing, thinking
  • Experience with Docker, container orchestration (Kubernetes), and job schedulers (Slurm)
  • Ability to work independently and collaboratively in a multi-cultural team
  • Excellent communication skills in a fast-moving environment
  • BSc, MSc, PhD or equivalent experience in Computer Science, Electrical Engineering or a related field

Nice to have

  • Experience with AMD tooling (not mandatory if strong fundamentals)
  • GPU kernel development experience with HIP, CUDA, or OpenCL
  • Tile-programming experience (Triton, Pallas, Gluon, Cutlass, cuDSL...)
  • Experience in multi-GPU cluster environments (single- and multi-node)
  • Background in high-performance networking for AI infrastructure
  • Familiarity with compiler backends or code generation
  • Experience with KVCache optimization and memory hierarchy tuning
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