Graybox is an early-stage MLOps startup building a model training solution for deep neural networks.
Our goal is to create a platform that simplifies the experimentation process, making it easier, faster, and more cost-effective for any AI developer.
We are an international team split between Paris and Zurich.
Why Join Us? Push the boundaries of AI and build something remarkable at Graybox. We’re pioneering ultra-efficient deep learning, developing neural networks 10x smaller and faster than today's state-of-the-art.
What You'll Do:
40% Engineering: Develop innovative code, test, and iterate features for our proprietary MLOps platform.
60% Research & Experimentation: Explore cutting-edge papers, run impactful experiments, and analyze results.
Key Responsibilities:
Develop compact, high-performance neural networks for 2D object detection (KITTI, COCO, Pascal VOC).
Create PyTorch layer wrappers and optimize dynamic hyperparameters during training.
Manage datasets and implement interpretability methods.
Benchmark and experiment with computer vision tasks (ImageNet, CIFAR100, YOLO).
Who We're Looking For:
Enthusiastic learners proficient in Python.
Passionate communicators fluent in English.
People who like bullet points after bold titles
What We Offer:
Flexibility in location, time commitment, and project scope.
Opportunity for conversion to a full-time role in an innovative environment.
Exciting projects you’ll definitely brag about.
Bonus Points:
Ready to Shape the Future of AI? Submit your resume and let us know why you’re excited to join us!
Experience with Training Deep Neural Networks in PyTorch / Tensorflow
Familiarity with Experiment Management Solutions (es. Weights & Biases)
