AI Research Scientist

Job Title

AI Research Scientist

Job Summary

We are seeking an AI Research Scientist to conduct cutting-edge research in artificial intelligence and machine learning. The successful candidate will develop Client algorithms, publish research, build prototypes, and translate research into production-ready AI solutions. This role requires a strong foundation in mathematics, deep learning, and scientific research, with experience in areas such as generative AI, large language models (LLMs), computer vision, reinforcement learning, or multimodal AI.

Key Responsibilities

• Conduct research in machine learning, deep learning, and artificial intelligence.
• Design and develop Client AI algorithms and model architectures.
• Build, train, fine-tune, and evaluate state-of-the-art AI models.
• Read, analyze, and implement research papers.
• Develop proof-of-concept (PoC) systems for emerging AI technologies.
• Publish research findings in conferences or journals (preferred).
• Collaborate with engineering teams to transition research into production.
• Evaluate model performance using appropriate benchmarks and metrics.
• Stay current with advancements in AI research and emerging technologies.
• Mentor junior researchers and engineers when appropriate.

Required Qualifications

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field.
• Strong background in machine learning, deep learning, and statistical modeling.
• Experience conducting research and implementing research ideas.
• Strong programming skills in Python.
• Excellent analytical and problem-solving skills.

Required Technical Skills

Programming

• Python
• C++ (preferred)
• SQL
• Git

Machine Learning & Deep Learning

• Supervised and unsupervised learning
• Reinforcement learning
• Representation learning
• Transfer learning
• Self-supervised learning
• Deep neural networks

AI Frameworks

• PyTorch
• TensorFlow
• JAX (preferred)
• Hugging Face Transformers

Mathematics

• Linear algebra
• Calculus
• Probability
• Statistics
• Optimization
• Numerical methods

Research Areas (one or more)

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Computer Vision
• Generative AI
• Diffusion models
• Vision-Language Models (VLMs)
• Multimodal AI
• Reinforcement Learning
• Graph Neural Networks (GNNs)
• Time-series modeling

LLM & Generative AI

• Prompt engineering
• Fine-tuning
• Retrieval-Augmented Generation (RAG)
• Agentic AI systems
• Synthetic data generation
• AI evaluation frameworks

Infrastructure

• Linux
• Docker
• Kubernetes (preferred)
• Distributed training
• GPU computing (CUDA)
• High-performance computing (HPC)

Cloud Platforms

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

Preferred Qualifications

• Ph.D. with publications in leading AI conferences or journals.
• Experience training large-scale foundation models.
• Contributions to open-source AI projects.
• Familiarity with distributed machine learning and model optimization.
• Experience with AI benchmarking and reproducible research.

Soft Skills

• Strong research and analytical thinking.
• Scientific writing and documentation.
• Collaboration across multidisciplinary teams.
• Curiosity and continuous learning.
• Presentation and communication skills.
• Mentoring and knowledge sharing.

Nice-to-Have Skills

• MLOps and model deployment
• Explainable AI (XAI)
• Responsible AI and AI safety
• Federated learning
• Edge AI
• Quantum machine learning (research-oriented)
• Knowledge graph applications
• Vector databases and semantic search

Common Tools

• PyTorch
• TensorFlow
• Hugging Face
• Weights & Biases
• MLflow
• Jupyter Notebook
• Docker
• GitHub
• Linux
• CUDA
• Ray
• DeepSpeed

Common Interview Topics

• Machine learning fundamentals
• Deep learning architectures
• Transformer architecture and attention mechanisms
• LLMs and foundation models
• Optimization algorithms (SGD, Adam, AdamW)
• Probability and statistics
• Linear algebra
• Research paper discussion and implementation
• Model evaluation and benchmarking
• Reinforcement learning fundamentals
• Distributed training
• Python coding
• System design for AI research
• Scientific reasoning and experimental design

Experience Levels

Junior Research Scientist (0–2 years)

• Master's degree or equivalent research experience.
• Strong academic projects in AI/ML.
• Familiarity with deep learning frameworks and research papers.

Mid-Level Research Scientist (2–5 years)

• Published research or equivalent industry research experience.
• Experience designing and evaluating Client models.
• Ability to lead research initiatives and prototype AI systems.