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Junior Deep Learning Engineer

Crisalix

Roles & Responsibilities

  • Quantitative degree in Computer Science, Engineering, Telecommunications, Data Science, Mathematics, Physics or related field
  • Solid understanding of deep learning fundamentals: loss functions, optimization, backpropagation, overfitting, regularization
  • Proficiency in Python and PyTorch (or another major DL framework)
  • Practical engineering skills: Git, GitHub (pull requests, code reviews), command line, and general software development workflow

Requirements:

  • Generate datasets and train models from scratch across the full model development lifecycle
  • Experiment with new algorithms and approaches to improve performance
  • Deploy models into production and contribute to ML infrastructure and tooling
  • Bridge research and engineering by reading papers and translating ideas into production-ready implementations

Job description

About the role:

The team


We are a multinational engineering organization focused on people and our product. At Crisalix we work hard, and we have a beautiful project ahead of us to make our company an excellent place to work.



The role


We are looking for a junior engineer to join the Deep Learning team. You will work across the full model development lifecycle: from generating datasets and training models from scratch, to experimenting with new algorithms and deploying them into production. This is a role where you will grow into an engineer who can bridge research and engineering: from reading a research paper to shipping it to production.

Our ideal candidate would have:

Essential Requirements


- Quantitative degree: Computer Science, Engineering, Telecos, Data Science, Mathematics, Physics or a related field that involves strong numerical and analytical foundations.

- Deep learning fundamentals: Solid understanding of how models are trained: loss functions, optimization, backpropagation, overfitting, regularization, etc.

- Python and PyTorch: Proficient in Python and hands-on experience training models in PyTorch or another major deep learning framework.

- Practical engineering skills: Comfortable with Git, GitHub (pull requests, code reviews), command line, and the general software development workflow. Should be able to pick up an open-source repo, set it up, and get it running.

- Research literacy: Ability and willingness to read, understand, and implement ideas from academic papers, journals.

We also value very positively:

Nice to Have


- Experience with computer vision (image classification, segmentation, landmark estimation, etc.).

- Experience with 3D reconstruction. Parametric models (SMPL), neural fields (NeRF, gaussian splats).

- Experience with generative models (training, fine-tuning, inference). Diffusion, GANs, VAEs, etc.

- Exposure to ML infrastructure and deployment (MLOps, model serving, containerization, CI/CD for ML).

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