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Senior Framework Engineer — Diffusion Inference

Finland, Helsinki · Job Posted April 05, 2026
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Job Description

As a Framework Engineer for Diffusion Model Inference, you will design, build, and evolve a production-grade inference framework for Diffusion Transformers (DiTs) powering state-of-the-art image and video generation. You will focus on framework-level engineering—model integration, scalable parallel inference, kernel plumbing, packaging, testing, and release management—ensuring diffusion workloads run out-of-the-box with exceptional performance on modern GPU systems.

Job Responsibility

  • Develop and maintain a diffusion inference framework for image/video generation with clean APIs and strong compatibility with widely used diffusion ecosystems
  • Own scalable parallel inference features for DiT workloads—single-node and multi-node
  • Integrate optimized operator backends (attention, GEMM, quantized paths) by bridging Python/C++ layers and ensuring correctness and high performance
  • Ship production-grade packaging & releases including containers, versioned artifacts, dependency hygiene, and pip-installable distributions
  • Build continuous testing & benchmarking infrastructure
  • Collaborate across the GPU software stack and translate framework needs into actionable upstream improvements
  • Support strategic customers by mapping real-world inference constraints into framework features, reference configurations, and reproducible deployment recipes
  • Communicate clearly around technical tradeoffs, performance bottlenecks, and roadmap decisions

Requirements

  • Strong Python and/or C++ engineering skills (debugging, profiling, testing, navigating complex codebases, clean abstractions)
  • Experience with ML frameworks—PyTorch strongly preferred, JAX/TF welcome—and familiarity with diffusion model execution
  • Proven ability to work in GPU-accelerated environments with intuition for performance, memory/compute tradeoffs, and profiling
  • Comfort with containers (Docker) and modern dev workflows (git, CI, build systems)
  • Strong cross-functional collaboration and clear technical communication skills
  • BSc, MSc, PhD, or equivalent experience in Computer Science, Electrical Engineering, or a related field

Nice to have

  • Experience with diffusion inference engines or parallel inference frameworks for DiTs (sequence, pipeline, CFG-parallel concepts)
  • Exposure to operator libraries such as AITER-style kernel collections (attention/GEMM/quant/comm)
  • GPU kernel development experience (HIP/CUDA/Triton) or familiarity with compiler/codegen backends
  • Knowledge of high-performance networking (RDMA, RoCE, InfiniBand, UCX) for multi-node inference
  • Experience building benchmarking and performance regression systems at scale

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