Deep Learning Expert
4 weeks ago
Arya.ai is a pioneering deep learning startup that has been at the forefront of AI innovation since its inception in 2013. Founded by Vinay and Deekshith from IIT Bombay, the company has evolved from providing open-source tools for deep learning to offering cutting-edge AI PasS for Banks, Insurers, and Financial Services. With a strong presence in the FSI sector, Arya.ai collaborates with over 100+ leading financial institutions globally.
The team at Arya.ai is committed to addressing the most pressing challenges in AI adoption, including AI explainability, safety, and alignment. With a world-class team of AI researchers, the company has made significant contributions to the community through its continuous research efforts.
Job Role
As a research scientist at Arya.ai, you will have the unique opportunity to work on large-scale industry problems and push the boundaries of AI technologies. You will be part of a dynamic team that combines startup culture with research innovation, ensuring speed and reliability in your work.
Responsibilities
- Work on advanced problems related to ML explainability, ML safety, and ML alignment.
- Have flexibility in choosing specialization areas within ML/DL and problem types that address the above challenges.
- Create new techniques around ML Observability & Alignment.
- Collaborate with MLEs and SDEs to roll out features and manage their quality until they are fully stable.
- Create and maintain technical and product documentation.
Qualifications
- Hold a solid academic background in machine learning and deep learning concepts.
- Have hands-on experience working with deep learning frameworks like TensorFlow, PyTorch, etc.
- Enjoy working on various DL problems involving different types of training data sets - textual, tabular, categorical, images, etc.
- Be comfortable deploying code in cloud environments or on-premise environments.
- Have strong fundamentals in MLOps and productionizing ML models.
- Prior experience in working on ML explainability methods - LRP, SHAPE, LIME, IG, CEM, etc.
- Have at least 2+ years of hands-on experience in Deep Learning or Machine Learning.
- Have hands-on experience in implementing techniques like Transformer models, GANs, Deep Learning, etc.
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