Artificial Intelligence/Machine Learning Engineer

As a Machine Learning Researcher at the company, you will be responsible for solving complex business problems through predictive modeling, deep learning, and generative AI research. Your key responsibilities will include: - Designing and architecting neural network models such as Transformers, CNNs, RNNs, and hybrid architectures, making decisions on layer configurations, attention mechanisms, activation functions, and connectivity patterns for optimal performance. - Developing and implementing training algorithms and optimization strategies, including custom loss functions, learning rate schedules, gradient clipping, and regularization techniques to ensure stable convergence and generalization. - Fine-tuning pre-trained foundation models using Parameter-Efficient Fine-Tuning methods for domain-specific applications. - Implementing Reinforcement Learning from Human Feedback and Constitutional AI methodologies. - Engineering high-quality training datasets through data collection strategies, cleaning pipelines, augmentation techniques, and synthetic data generation. - Designing and executing comprehensive model evaluation frameworks and developing Retrieval-Augmented Generation architectures. - Optimizing model architectures for efficiency through knowledge distillation, model pruning, quantization-aware training, and neural architecture search. - Performing rigorous statistical analysis and hypothesis testing on model outputs, identifying failure modes, error analysis, and edge cases requiring architectural improvements. - Collaborating with domain experts to translate business requirements into mathematical formulations and ML problem statements. - Mentoring junior researchers and engineers on machine learning theory, algorithmic best practices, and research methodologies. - Documenting research findings, model architectures, training methodologies, and experimental results in technical reports. In terms of qualifications and experience, you should have: - Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or related quantitative field. Master's degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, or related field highly desirable. - Strong foundation in linear algebra, calculus, probability theory, statistics, and optimization theory. - Minimum 5-9 years of research and development experience in applied machine learning with expertise in designing and training neural networks. - Extensive experience in at least two domains such as Natural Language Processing (NLP), Computer Vision, Speech Recognition, Recommendation Systems, or Reinforcement Learning. Your technical competencies should include: - Deep theoretical understanding of machine learning algorithms, deep learning architectures, and statistical modeling. - Proficiency in PyTorch or TensorFlow for implementing custom models and training loops. - Knowledge of transformer architectures, mathematical optimization, reinforcement learning, generative modeling, and model compression. - Understanding of research and development tools for experiment tracking, data manipulation, NLP libraries, visualization, version control, and hardware awareness. This role offers a challenging opportunity to contribute to cutting-edge research in machine learning and AI within a dynamic and innovative environment. As a Machine Learning Researcher at the company, you will be responsible for solving complex business problems through predictive modeling, deep learning, and generative AI research. Your key responsibilities will include: - Designing and architecting neural network models such as Transformers, CNNs, RNNs, and hybrid architectures, making decisions on layer configurations, attention mechanisms, activation functions, and connectivity patterns for optimal performance. - Developing and implementing training algorithms and optimization strategies, including custom loss functions, learning rate schedules, gradient clipping, and regularization techniques to ensure stable convergence and generalization. - Fine-tuning pre-trained foundation models using Parameter-Efficient Fine-Tuning methods for domain-specific applications. - Implementing Reinforcement Learning from Human Feedback and Constitutional AI methodologies. - Engineering high-quality training datasets through data collection strategies, cleaning pipelines, augmentation techniques, and synthetic data generation. - Designing and executing comprehensive model evaluation frameworks and developing Retrieval-Augmented Generation architectures. - Optimizing model architectures for efficiency through knowledge distillation, model pruning, quantization-aware training, and neural architecture search. - Performing rigorous statistical analysis and hypothesis testing on model outputs, identifying failure modes, error analysis, and edge cases requiring architectural improvements. - Collaborating with domain experts to translate business requirements into