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Machine Learning Engineer - Computer Vision

Repli5 AB

Västra Götalands län, Göteborg

Previous experience is desired

8 days left
to apply for the job

The Role: We're looking for an experienced Machine Learning Engineer with a deep background in computer vision and generative AI. You’ll be part of a team developing systems that generate realistic, diverse, and highly controllable synthetic image data using state-of-the-art tools like Stable Diffusion, LoRA, and ControlNet. Your work will directly impact the performance and robustness of real-world AI systems—from self-driving cars to defense robotics.

What You’ll Do

  • Develop and fine-tune generative AI pipelines (e.g., Stable Diffusion, ControlNet, LoRA) for synthetic image generation.
  • Design and implement machine learning workflows to generate structured, photorealistic datasets.
  • Collaborate with 3D artists and simulation engineers to align ML models with physical environments and sensor setups.
  • Work closely with customers in automotive, robotics, and aerospace to deliver tailored datasets.
  • Optimize generation systems for scalability, quality, and speed using tools like ComfyUI or custom pipelines.

You Should Have

  • MSc or PhD in Computer Vision, Machine Learning, or related field.
  • 3+ years of hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Experience working with generative AI models (especially diffusion-based models).
  • Familiarity with tools like LoRA, ControlNet, ComfyUI, or custom workflows around Stable Diffusion.
  • Strong understanding of synthetic data generation and its role in training perception systems.
  • A track record of deploying ML systems in domains like automotive, robotics, defense, or manufacturing.

Nice to Have

  • Experience working with 3D simulation pipelines or sensor modeling (e.g., depth, LiDAR, segmentation).
  • Contributions to open-source ML/gen AI tools or custom diffusion model training.
  • Enjoying reading research papers
  • A problem solving intellect and a team player spirit
  • Knowledge of synthetic-to-real domain adaptation techniques.
  • Experience in startup environments or working directly with enterprise R&D teams.
  • A positive can-do attitude
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