Job Title:
Applied Scientist
Company: Meril
Location: Kurnool, Andhra Pradesh
Created: 2026-01-11
Job Type: Full Time
Job Description:
Applied Scientist Location:Bangalore / Hybrid Team:AIRole Overview We are seeking anApplied AI Scientistto 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