AI Research Intern

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Entry From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Series B From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

AI Research Intern Lausanne, Canton de Vaud, Switzerland, Paris About Us 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. Responsabilities - Design 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. Requirements - Technical 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

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