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
