Actionable Insights with Industrial-Grade Video Understanding
AI for the Physical World. At Ramblr, we go beyond superficial video analysis to extract deep context from egocentric videos. Our technology provides a comprehensive understanding of actions, individual objects, and their relationships. Prompt Ramblr’s AI assistant to unlock precise insights and pinpoint specific moments in thousands of hours of multimodal videos captured from a first-person perspective. Are you excited to become a Ramblr and join us at the intersection of AI and spatial computing? If so, you can apply directly to the job posting or use the open application form.
We look forward to hearing from you !We are looking for a strong deep learning engineer who can deploy and maintain state-ot-the-art AI models. You write clean, efficient and highly parallelized code to improve and extend our deep learning stack. You work on improving our platform to make training and deployment of models more efficient.
By applying your understanding of classical computer vision and state-of-the-art deep neural networks you are responsible for the performance and reliability of our platform. You build and optimize parts of our model stack and efficiently integrate them into our overall pipeline.
Passion for solving the hard problems in deep learning
Required:
Ability to write efficient code for highly parallelized data loading and transformation, model serving and training, stacking models into efficient serving graphs and more
Full proficiency in python-based collaborative software engineering: follow consistent style-guide, clean design patterns, self-documented code, unit/integration tests, type annotations
Machine Learning: data loading pipelines, serving/deployment, neural network architectures (especially Transformer / CNNs), model optimization, PyTorch
Computer Vision: CNNs, Vision Transformers, spatio-temporal data, image- and video embeddings, image augmentation
Experience with scientific python libraries such as: numpy, openCV, matplotlib, scipy, scikit-learn, scikit-optimize
General: git VCS, code reviews, development on Linux, distributed computing concepts
Optional:
Natural Language Processing: Transformer architectures, vision-language alignment, prompt engineering
