Machine Learning Intern at Aftershoot

New Delhi, delhi, India -

Full Time

Start Date

Immediate

Expiry Date

19 Sep, 26

Salary

0.0

Posted On

21 Jun, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Generative AI, Deep Learning, Python, PyTorch, TensorFlow, CNNs, GANs, VAEs, Diffusion Models, Image Segmentation, Inpainting, Text-to-Image Models, OpenCV, Hugging Face, FastAPI

Industry

Software Development

Description

At Aftershoot, we’re building more than just AI tools; we’re building a global community of photographers who spend less time behind their screens and more time behind the lens. Every feature we create, every update we ship, is designed to make post-processing easier, faster, and smarter for photographers worldwide, from culling to editing to retouching. Our AI tools do all the heavy lifting, so photographers can focus on what they do best: capturing moments, telling stories, and creating magic. We’re a fast-growing, passionate team backed by over 100K+ active users globally. But we’re just getting started. Our mission is clear: solve the biggest pain points in photography, and keep evolving with photographers who use our tools daily. If you care deeply about building meaningful products, working with curious and driven teammates, and being part of a team that genuinely loves what they do, we’d love to meet you. Let’s create the future of AI in photography, together. We're looking for a passionate and driven Machine Learning Intern to join our team in New Delhi for 6 months. 🚀 Your Mission You'll work closely with our experienced ML engineers to research, prototype, and build innovative solutions in Computer Vision and Generative AI. This is a hands-on opportunity to contribute to real-world projects that push the boundaries of visual understanding and generation. 🎯 What You Will Be Doing Assist in the design, development, and evaluation of deep learning models for vision-based use cases. Experiment with architectures like CNNs, GANs, VAEs, and diffusion models. Contribute to data preprocessing, augmentation, and visualization workflows. Collaborate with cross-functional teams (Product, Engineering, Design) to iterate quickly on ideas. Conduct literature reviews and apply the latest research techniques in Generative AI and image processing. Help build reproducible training/inference pipelines using PyTorch or TensorFlow. Participate in code reviews, team discussions, and knowledge-sharing sessions. 💪 What We’re Looking For Currently pursuing or recently completed a Bachelor’s/Master’s degree in Computer Science, Electrical Engineering, AI, or related field. Strong understanding of Deep Learning fundamentals, especially CNNs and image-based architectures. Familiarity with one or more of the following: GANs, VAEs, diffusion models, image segmentation, inpainting, text-to-image models. Proficient in Python, with experience in PyTorch or TensorFlow. Hands-on experience with at least one computer vision or generative model project (coursework or personal). Ability to write clean, maintainable, and modular code. Eagerness to learn, experiment, and build in a fast-paced environment. ⭐ Bonus Skills Experience with OpenCV, Hugging Face, or ONNX. Exposure to tools like Weights & Biases, Streamlit, or FastAPI. Contributions to GitHub repositories or Kaggle competitions. Familiarity with optimization techniques for model inference on edge devices. What You'll Gain Mentorship from experienced ML engineers working on state-of-the-art problems. Exposure to the full ML lifecycle – from research to deployment. Real-world experience with impactful, production-grade vision systems. Opportunity to convert into a full-time role based on performance in these 6 months. Apply With Resume. GitHub/Portfolio (if available). Brief paragraph on why you’re interested in this role.

Responsibilities

Research, prototype, and build innovative solutions in Computer Vision and Generative AI. Assist in designing deep learning models and building reproducible training and inference pipelines.