AI algorithm engineer

About the RoleThis role involves deep technical expertise, strong research capabilities, and the ability to translate research into scalable, production-ready solutions.ResponsibilitiesModel Development & OptimizationDesign, implement, and train large-scale neural network models (e.g., Transformer-based architectures) for NLP, computer vision, or multi-modal tasks.Optimize model architectures for efficiency, scalability, and inference performance.Algorithm Research & InnovationConduct research on SOTA (state-of-the-art) algorithms, such as reinforcement learning, self-supervised learning, and parameter-efficient fine-tuning.Experiment with new training strategies, model compression, and distributed training techniques.Data Processing & Pipeline ManagementBuild and maintain data pipelines for large-scale datasets, including cleaning, augmentation, and labeling workflows.Ensure data quality and diversity for robust model training and evaluation.Deployment & IntegrationCollaborate with the engineering team to deploy models in production environments.Optimize model serving for latency, scalability, and cost-efficiency.Cross-functional CollaborationWork closely with product managers, data scientists, and software engineers to align model performance with business needs.Provide technical guidance and mentorship to junior team members.QualificationsRequired:Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or related fields.Strong expertise in deep learning frameworks such as PyTorch, TensorFlow, or JAX.Proven experience with large-scale model training, including distributed training and parallel computing frameworks (e.g., DeepSpeed, Megatron-LM, or Horovod).Solid understanding of Transformer architectures and optimization techniques.Proficiency in Python and at least one other programming language (e.g., C++ or Rust).Strong mathematical foundation in linear algebra, probability, optimization, and statistics.Preferred:Publications in top AI/ML conferences (NeurIPS, ICLR, ICML, CVPR, ACL, etc.).Experience with MLOps tools (e.g., MLflow, Kubeflow, Ray) and cloud platforms (AWS, GCP, Azure).Hands-on experience with quantization, pruning, or model distillation for efficiency optimization.Familiarity with multi-modal AI, RLHF (Reinforcement Learning with Human Feedback), or fine-tuning techniques for foundation models.