Research Engineer
12 hours ago
Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.
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 models- Explore multiple areas of AI research, old and new, and develop end-to-end ML pipelines to solve use cases- Build 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 environment- Work with Solution-Architects to build cutting edge solutions, benchmark various baselines and techniques- Document 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-how- Hands-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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