Software Engineer Internship

Allus AI (YC F25); San Jose (CA); On-site; USD 80,000 - 130,000; Full time

Job summary

Allus AI (YC F25) in San Jose is seeking talented individuals excited about building the next generation of vision intelligence systems. Candidates should have strong programming skills in Python and experience with machine learning or computer vision projects.

The ideal candidate will have a relevant BS, MS, or PhD and a willingness to learn quickly in a startup environment. Familiarity with frameworks like PyTorch will be advantageous.

Qualifications

• Pursuing a relevant degree in Computer Science, Machine Learning, or related field.
• Strong programming skills in Python.
• Experience with ML or computer vision projects.

Skills

Python programming

Machine Learning

Computer Vision

Problem-solving

Education

BS, MS, or PhD in relevant fields

Tools

PyTorch

Job description

Allus AI is building the next generation of vision intelligence systems.

What we’re looking for

• 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 have

• 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

Relevant experience

• Computer vision projects
• Machine learning research
• Foundation model fine‑tuning
• Dataset generation and curation
• Model evaluation and benchmarking
• MLOps and training infrastructure
• Open‑source ML contributions