Applied AI / ML Research Scientist

Research Engineer, Applied Research (Biotech AI – Drug Discovery) About the Company Quantiphi is an award-winning AI-first digital engineering 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. Quantiphi has seen 2.5x growth YoY since its inception in 2013 to 3500+ team members globally. For more details, please visit our

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About the Applied Research Unit Applied Research is an R&D practice at Quantiphi focused on advancing the frontiers of AI technologies with Applied Machine Learning at its core. We ideate and build novel solutions to high-impact, cutting-edge challenges, with a focus on advanced prototyping and scalable proof of concepts. Within this unit, the AI-Accelerated Drug Discovery practice is a key pillar that aims to apply state-of-the-art AI methodologies to revolutionize the way new therapeutics are discovered and developed. We are committed to driving meaningful scientific breakthroughs by combining strong AI research with deep cross-disciplinary collaboration.

Job Description Role Level : Research Engineer Work Location : India Resource Count : 2

The Role This is a unique opportunity to work on scientifically impactful problems at the intersection of AI and biotechnology within Quantiphi Applied Research team. In this role, you will work on the development of core AI models and algorithms aimed at accelerating the drug discovery process. The position focuses on advancing foundational AI techniques such as generative modeling, optimization, and reinforcement learning, applied to molecular and bio-pharmaceutical data. 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 in Healthcare and Life Sciences 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. While prior experience in the biotech or life sciences domain is highly valued and will elevate the candidate profile, we are equally open to exceptional AI / ML researchers from other domains who are excited to explore and learn the nuances of this rapidly growing field

• Please note : This is a core AI research role, not a software engineering or system integration position. We are particularly keen to engage with candidates focused on scientific AI innovation rather than application development or LLM / GenAI-centric workflows
• Responsibilities Stay ahead of the AI research curve, focusing on foundational AI methodologies applicable to drug discovery and molecular design. Build rapid prototypes, conduct detailed experimental studies, and develop advanced AI models in areas such as generative modeling, reinforcement learning, graph-based learning, and molecular property prediction. Work closely with interdisciplinary teams including biologists, chemists, and life science domain experts to design scientifically sound AI approaches. Contribute to Quantiphi IP portfolio through the development of novel algorithms, proof of concepts, and potential publications. Drive thought leadership through documentation, knowledge dissemination, and participation in conferences, blogs, webinars, and publications. Publish Research papers in prestigious Conferences and Journals Requirements Must Have :

Master’s degree, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields. Minimum work experience required : from new graduates to 3+ yrs of research experience post graduation (in ML research) Strong foundation in AI / ML concepts with hands-on experience in model development, experimental design, and large-scale data analysis. Excellent in-depth understanding of ML concepts and the respective underlying mathematical know-how. Working knowledge of using NLP with biological sequences. Solid research mindset with a track record of working on complex AI problems—experience with drug discovery datasets is a plus but not a prerequisite. Excellent programming skills in Python, with experience using AI / ML frameworks like PyTorch or TensorFlow. Hands-on experience in developing and deploying models with various deep learning architectures in multiple ML areas like Computer-Vision, NLP, Statistics etc Ability to independently learn new scientific domains and apply AI techniques to novel bio-pharmaceutical problems. Strong communication skills with the ability to present complex ideas in an accessible format across audiences. Ability to translate abstract highlights into understandable insights in multiple knowledge-dissemination formats like blogs, presentations, paper-publications, tutorials and webinars Good to Have :

Prior exposure to molecular datasets, cheminformatics, bioinformatics, or life sciences. Hands-on experience with insilico techniques in drug discovery Hands-on experience with HPC workflows with genome datasets Familiarity with generative chemistry models, graph neural networks, reinforcement learning, or multi-objective optimization. Demonstrated industry research experience will be considered as an additional bonus. Research publications in AI / ML conferences such as NeurIPS, ICML, ICLR, or relevant bioinformatics journals Experience with cloud environments like GCP or AWS and scalable model training. Strong classical education on math / physics / mechanics / CS / Engineering concepts will also be an advantage. Why Join Us? Opportunity to work at the cutting edge of AI and biotechnology, solving problems with real-world scientific impact. Exposure to interdisciplinary teams and a culture that encourages continuous learning and exploration. Contribute to an R&D environment that values curiosity, innovation, and the advancement of AI for good.