Computer Vision Engineer

Job Description

Software Engineer Internship | Allus AI (YC F25)

The Tone:
This is an internship at Allus AI, located in Atlanta, GA. Our company is focused on building the next generation of vision intelligence systems. This role offers a unique opportunity for aspiring engineers to contribute directly to the development of our core technology. Interns will gain hands-on experience in a startup environment, taking ownership of projects that impact production systems.

The TL;DR
• Role: Internship
• Location: In-person, Atlanta, GA
• Mission: Contribute to the development and implementation of cutting-edge vision intelligence systems.
• Tech Stack: Python, PyTorch

What You’ll Actually Do
• Contribute: Develop and contribute to ML projects, research initiatives, or open-source efforts involving computer vision.
• Build: Participate in building and iterating on production systems for vision intelligence.
• Manage: Engage in the generation and curation of datasets for machine learning models.
• Evaluate: Perform model evaluation and benchmarking of computer vision architectures.
• Support: Assist with MLOps practices and optimize training infrastructure.

The Must-Haves
• Background: Student pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Machine Learning, Electrical Engineering, Robotics, or a related field.
• Experience: Demonstrated experience building ML projects, research projects, or open-source contributions specifically involving computer vision.
• Skills: Strong programming proficiency in Python; familiarity with PyTorch or similar deep learning frameworks; understanding of modern computer vision architectures and training workflows; strong problem-solving ability and eagerness to learn quickly.
• Bonus: Experience training or fine-tuning vision models; experience with object detection, segmentation, OCR, or multimodal systems; familiarity 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, or Qwen-VL.