Applied Scientist

Applied Scientist

Location: Bangalore / Hybrid

Team: AI

Role Overview

We are seeking an Applied AI Scientist to work on end-to-end applied research and build scalable AI systems using Machine Learning, Deep Learning, and Large Language Models (LLMs). This role focuses on transforming complex problems into practical, high-impact solutions through research, experimentation, and engineering.

Key Responsibilities

• Design and execute applied research projects using ML, DL, and LLM techniques
• Collaborate with cross-functional teams to define open-ended problems and deliver data-driven solutions
• Develop, prototype, and optimize models on large-scale structured and unstructured datasets
• Contribute to internal research infrastructure, tools, and model deployment pipelines
• Stay up to date with advancements in AI research and industry best practices
• Translate research findings into reliable, production-ready systems

What You’ll Gain

• Exposure to real-world applied AI challenges with measurable impact
• Access to large datasets and high-performance computing resources
• A collaborative environment that values research rigor and innovation
• Opportunities for mentorship, knowledge sharing, and professional growth
• Support for publishing, open-source contributions, and external collaborations

Qualifications

• PhD or Master’s degree in Computer Science, Applied Mathematics, Statistics, Physics
• 3+ years of experience building ML, DL, RL, or LLM-based systems in research or industry
• Strong foundation in statistics, optimization, and numerical methods
• Experience with time-series modeling, NLP, or high-dimensional data
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, JAX, or Hugging Face
• Familiarity with distributed training, MLOps practices, and version control (Git)
• Strong communication and collaboration skills

Preferred Qualifications

• Publications in leading AI conferences (NeurIPS, ICML, ICLR, KDD, etc.)
• Experience working with large-scale or noisy datasets
• Background in applied research environments