Summary
Allus AI is building the next generation of vision intelligence systems, and they are seeking Software Engineer Interns. The role involves working with machine learning and computer vision technologies, requiring strong programming skills and a passion for innovation.
Skills
- Pursuing a BS, MS, or PhD in Computer Science, Machine Learning, Electrical Engineering, Robotics, or a related field
- Strong programming skills in Python
- Experience building ML projects, research projects, or open-source contributions involving computer vision
- Familiarity with PyTorch or similar deep learning frameworks
- Understanding of modern computer vision architectures and training workflows
- Strong problem-solving ability and willingness to learn quickly
- Excited about startups, ownership, and building production systems
- Experience training or fine-tuning vision models
- Experience with object detection, segmentation, OCR, or multimodal systems
- Experience with distributed training or GPU optimization
- Publications, research experience, Kaggle competitions, or open-source contributions
- Familiarity with foundation models such as CLIP, DINOv2, SAM, Florence, Qwen-VL, or similar systems
Qualifications
Must Haves
- Pursuing a BS, MS, or PhD in Computer Science, Machine Learning, Electrical Engineering, Robotics, or a related field
- Strong programming skills in Python
- Experience building ML projects, research projects, or open-source contributions involving computer vision
- Familiarity with PyTorch or similar deep learning frameworks
- Understanding of modern computer vision architectures and training workflows
- Strong problem-solving ability and willingness to learn quickly
- Excited about startups, ownership, and building production systems
Nice to Haves
- Experience training or fine-tuning vision models
- Experience with object detection, segmentation, OCR, or multimodal systems
- Experience with distributed training or GPU optimization
- Publications, research experience, Kaggle competitions, or open-source contributions
- Familiarity with foundation models such as CLIP, DINOv2, SAM, Florence, Qwen-VL, or similar systems