About the Applied Research Unit
Applied-Research is an R&D practice at Quantiphi, focused on unraveling the frontiers of Al 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 Al to understand and transform the way humans collaborate with Al-systems in the near future.
The Role in a Nutshell: This is a great opportunity to work on cutting-edge topics within the growing Applied Research team of Quantiphi. You will investigate some of the latest trends in artificial intelligence, esp using the state of the art deep learning techniques, LLMs, knowledge graphs etc, develop proof of concepts, prototypes, publish your work and produce Intellectual Property (IP)
Responsibilities:
● Stay ahead of the AI maturity curve, focusing on the upcoming areas of AI research, curate new ideas and upcoming research areas, explore multiple areas of Al research
● Build rapid prototypes and conduct detailed experimental studies to prove concepts at the intersection of ML, Engineering and operations research (OR).
● Work with experienced researchers and engineers to build cutting edge solutions, benchmark various novel baselines and models using multi-objective optimization and reinforcement learning ● Contribute to Q’s growth through new ideas, reusable building blocks/assets, and IP/patents
● Enhance company brand through thought leadership, document the knowledge gained and disseminate to broader audience in multiple formats, working on technical content creation, 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 Al 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.
Must have:
● Education level: Master’s or PhD in Mech/Chem/Industrial/Comp engineering streams with focus on operations research (OR) and/or industrial optimization
● Minimum work experience required: 3-5 yrs of research experience post graduation specially in optimization / OR
● Hands-on experience in developing deploying optimization algorithms for industrial problems / benchmarks
● Excellent in-depth understanding of discrete / continuous optimization concepts such as LP, QP, PSO, GA and the respective underlying mathematical know-how
● Familiarity with diverse optimization tools and libraries Google OR, pyomo, pymoo, Gurobi, pyswarms
● Experience of submitting papers to applied AI / engineering journals or machine learning conferences (e.g. NeurIPS, ICLR, ICML, etc.)
● Excellent coding skills (Python advanced) and flexible mindset, with ability to quickly switch between & adapt to newer concepts
● Knowledge of Cloud-environments like GCP/AWS and ML frameworks like TensorFlow/PyTorch, with good experience in large scale distributed training
● Ability to translate abstract highlights into understandable insights in multiple knowledge-dissemination formats like blogs, presentations, paper-publications, tutorials and webinars
Good to have:
● Demonstrated industry experience in OR will be considered as a plus.
● Familiarity with blending ML, DL algorithms with optimization or operation research will be an advantage