Quantiphi | Research Engineer
1 week ago
About the Unit:Applied-Research is an R&D practice at Quantiphi, focused on unraveling the frontiers of AI technologies with Applied ML at its core. With a multifaceted R&D strategy, we tackle ideating and building innovative solutions to cutting edge challenges, with advanced prototyping and scalable proofs of concept. Strengthened by the strategic partnerships working towards a generalized goal, we strive to leverage and demonstrate the major advances in AI to understand and transform the way humans collaborate with AI-systems in the near future.
Responsibilities:Working with structured and unstructured data to build Traditional and Deep-Learning based modelsExplore multiple areas of AI research, old and new, and develop end-to-end ML pipelines to solve use casesBuild rapid prototypes and conduct detailed experimental studies to prove concepts in multiple ML domains like Computer-Vision, NLP, Reinforcement Learning etc., in a well defined time-bound environmentWork with Solution-Architects to build cutting edge solutions, benchmark various baselines and techniquesDocument the knowledge gained and disseminate to broader audience in multiple formats, working on technical content creation and publication, in conjunction with content-team and program managers
Requirements:The position involves working with a diverse, lively, and proactive group of nerds who are constantly raising the bar on translating the latest AI research into tangible reusable assets for the community. Hence this would require a high level of conceptual understanding, attention to detail and agility in terms of adaptation to new technologies.Excellent in-depth understanding of ML concepts and the respective underlying mathematical know-howHands-on experience in developing and deploying models in multiple ML areas like Computer-Vision, NLP,LLM,etc.Knowledge of Cloud-environments like GCP/AWS and ML frameworks like TensorFlow/PyTorch, with good experience in large scale distributed training.Excellent coding skills and flexible mindset, with ability to quickly switch between & adapt to newer concepts.Ability to translate abstract highlights into understandable insights in multiple knowledge-dissemination formats like Blogs, Presentations, Paper-Publications, Tutorials and Webinars.Prior R&D experience, and/or publications at top-tier ML conferences will be a huge advantage.
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