Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
ResponsabilitiesDesign and implementation of deep learning models for computer vision tasks.
Research and experimentation with CNNs and Vision Transformers.
Model compression techniques such as knowledge distillation.
Quantisation-aware training (QAT) and post-training quantisation (PTQ).
Network and dataset pruning strategies.
Design of efficient architectures for edge and embedded systems.
Dataset curation, balancing, and bias mitigation.
Experimental design, ablation studies, and reproducibility practices.
Robust evaluation using appropriate metrics (e.g., mAP, IoU, calibration).
Failure case analysis and robustness testing under distribution shifts.
You will be encouraged to read, analyse, and implement ideas from leading conferences such as CVPR, ICCV, ICLR, and NeurIPS.
RequirementsTechnical Background: Solid foundation in Deep Learning (preferably PyTorch).
Experience with CNNs and/or Transformers (academic or project-based).
Understanding of bias–variance trade-offs and generalisation.
Familiarity with optimisation fundamentals and basic probability.
Experience or strong interest in model compression techniques.
Interest in hardware-aware and efficient model design.
Experience with ONNX, TensorRT, TFLite or LiteRT.
Familiarity with experiment tracking tools (e.g., W&B, MLflow).
Experience conducting ablation studies.
Exposure to dataset curation or annotation processes.
Prior participation in research projects or conference work.
Hands-on research experience in applied computer vision.
Exposure to model optimisation and edge deployment challenges.
Experience reading, implementing, and evaluating cutting-edge research.
Mentorship from experienced researchers and engineers.
Opportunity to contribute to publications or conference submissions.
This internship will be based either in Paris (France) OR in Lausanne (Switzerland).
We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.
Skills Required
- Solid foundation in Deep Learning (preferably PyTorch)
- Experience with CNNs and/or Transformers (academic or project-based)
- Understanding of bias-variance trade-offs and generalisation
- Familiarity with optimisation fundamentals and basic probability
- Experience or strong interest in model compression techniques
- Interest in hardware-aware and efficient model design
