Job TitleAI Research ScientistJob SummaryWe 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 SkillsProgramming- 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 LevelsJunior 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.
