Representation Learning - Research Scientist


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Our client specializes in deep learning and visual domain machine learning at scale. They have a team of scientists and engineers scale solutions for top global companies and their mission is to build and operate massively scalable systems to tackle some of today's hardest problems. Decades of software and ML expertise, have given valuable clients, helping them to build some of the most widely used products in the world.

Job Description

Interested in working on hard problems at scale? We are looking for researchers with 3+ years of practical Industry experience, exceptional coding, and software engineering skills; Ph.D. level research and/or a strong publication record (at conferences like ICLR, NeurIPS, ICML, CVPR, ICCV, etc or similar peer-reviewed journals). As a specialist with experience in this area, you will conduct original research and development of techniques to apply this emerging field to large scale problems involving real-world data across a variety of image and video domains. What you'll do? You will likely work on both applied and theoretical problems at different times, based on your interests and the needs of the business. We have an international team from diverse academic backgrounds and strongly believe in cross-pollination across projects, disciplines, and domains. We believe that testing theories at scale is one of the best ways to push forward the field, that's why you'll play a part in our work of exploring applications of some of our more theoretical ideas You will have the opportunity to publish at Computer Vision and ML conferences if your work yields results. We are committed to open source code and (when possible) datasets: we regularly attend and publish our work at top conferences like ICLR, NeurIPS, CVPR, and more. What are we looking for? We are searching for talent with demonstrated academic/industrial research background and experience & interest in one or more of the following areas is desirable: * Graph Neural Networks; Knowledge-based Neural Networks * Generative models (GANs, VAEs, invertible flows, Autoregressive Image models) * Language modelling - word/sentence embeddings * Self-supervised and unsupervised learning * Content-based Image retrieval * Metric learning * Cognitive Science, perception, semantic understanding and parsing * Multi-task learning * Distributed and Large scale training * Active Learning; applied Reinforcement learning * Time series analysis; temporal hierarchies * Few-shot learning; domain adaptation * Meta-learning; Neural architecture search; model compression * Theoretical analysis of Neural Network architectures * Distributed and Large scale training * Applied ML for Computer Vision tasks (Classification, Regression, Segmentation, Detection, Recognition) on Images and Video Skills and experience: - We work primarily in Python for research, with PyTorch and Tensorflow being our preferred tools. - We also strive for a high degree of programming competence within our research group, as we have found that good discipline in implementing ideas, along with good documentation, makes your task easier and collaboration more pleasant. As a researcher in a rapidly growing startup, your responsibilities will be fairly broad: we have a fairly flat structure and encourage everyone to wear multiple hats based on their talents. Many of our researchers have PhDs or post-docs in relevant fields, often combined with industry experience. While not a strict requirement, having published in a relevant academic context is useful; but equally desirable is having a demonstrable codebase of quality projects. *** NOTE: When applying please mention your relevant publications (and ideally point to your open source repos) to help us quickly get an overview of your work.


Only candidates from United States, Canada, Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Mexico, Panama, Peru, Uruguay

Intermediate or advanced spoken English is required for ALL opportunities. If you can't speak English yet, please keep practicing and apply in the future.

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