ML Research Scientist

Arya.ai is one of the first deep learning startups globally. Vinay and Deekshith from IIT Bombay started it in 2013. We started as an open-source tool provider for deep learning in 2015 and are now offering one of the most verticalized AI PasS for Banks, Insurers and Financial Services. We work with more than 100+ FSI logos across the globe.

We at Arya.ai solve the most critical problems in AI adoption, namely, AI explainability, safety, and alignment. We have gathered a world-class team of artificial intelligence researchers with multiple domains of expertise in various deep learning and machine learning segments. Through our continuous research on complex problems, we have contributed many new advancements to the community.

As a research scientist at Arya.ai, you will be uniquely positioned in our team to work on very large-scale industry problems and push forward the frontiers of AI technologies. You will become a part of the unique atmosphere where startup culture meets research innovation, with key outcomes of speed and reliability.

Responsibilities

1. You'll work on advanced problems related to ML explainability, ML safety, and ML alignment.
2. You'll have flexibility in picking up the specialization areas within ML/DL and problem types that address the above challenges.
3. Create new techniques around ML Observability & Alignment.
4. Collaborate with MLEs and SDE to roll out the features and manage their quality until they are fully stable.
5. Create and maintain technical and product documentation.

Qualifications

1. Has a solid academic background in concepts of machine learning and deep learning.
2. Hands-on experience in working with deep learning frameworks like Tensorflow, Pytorch etc
3. Enjoys working on various DL problems that involve using different types of training data sets - textual, tabular, categorical, images etc
4. Comfortable deploying code in cloud environments/on-premise environments.
5. Strong fundamentals in MLOps and productionising ML models.
6. Prior experience on working on ML explainability methods - LRP, SHAPE, LIME, IG, CEM etc.
7. 2+ years of hands-on experience in Deep Learning or Machine Learning.
8. Hands-on experience in implementing techniques like Transformer models, GANs, Deep Learning, etc.
9. Should've published papers in the domain of Deep Learning & ML.