Join the Deep Learning team and work across the full model development lifecycle.
Generate datasets and train models from scratch.
Experiment with new algorithms and deploy them into production.
Bridge research and engineering by reading research papers and shipping ideas to production.

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.
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).

Junior Deep Learning Engineer

Key skills