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AI & Computer Vision Intern - Data augmentation

Harmattan AI - Lausanne, Canton de Vaud, Switzerland

Posted Mar 20, 2026

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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Market context

U.S. role benchmark (BLS OEWS)
$111,944 U.S. median for this role
Projected growth (BLS Employment Projections)
+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

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

Role

Role function
Data 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

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About this role

AI & Computer Vision Intern - Data augmentation Lausanne, Canton de Vaud, Switzerland 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. About the Role To train highly robust perception systems, we need aerial imagery spanning a massive variety of conditions. You will engineer an advanced Generative AI pipeline capable of completely transforming the context of existing datasets; shifting time-of-day (day to night), changing seasons (summer to winter), or altering entire biomes and weather systems; while perfectly preserving small, critical target objects like drones. - Refine the Generative Architecture: Take ownership of a sophisticated multi-pass diffusion pipeline (structural background replacement + lighting/atmospheric glazing) to seamlessly adapt scene contexts while maximizing physical realism. - Solve Edge Cases: Improve custom masking and high-res depth-patching algorithms (e.g., histogram matching, seamless blending) to anchor small objects in 3D space, eliminating generative artifacts and "sticker" effects. - Scale & Validate: Generate large-scale augmented datasets and rigorously quantify their impact on downstream model performance. Design experiments to measure how the inclusion and varying ratios of this synthetic data directly improve the

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