AI Research Engineer

Core Responsibilities Model Development : Implement, fine-tune, and evaluate advanced AI architectures (e.g., LLMs, Vision-Language Models, Diffusion Models). Scalable Training : Design and manage distributed training pipelines across massive GPU/TPU clusters. Optimization : Accelerate model inference and reduce memory footprints using quantization, distillation, and compilation techniques. Data Engineering : Build robust pipelines for data curation, deduplication, and synthetic data generation. Research Translation : Evaluate academic literature and rapidly prototype novel algorithms to solve complex business problems. Required Qualifications Education : Master’s or Ph.D. in Computer Science, Data Science, Mathematics, or a related quantitative field (or equivalent practical experience). Programming : Mastery of Python and strong familiarity with low-level systems languages like C++. Frameworks : Deep expertise in deep learning frameworks such as PyTorch, JAX, or TensorFlow. Distributed Computing : Hands-on experience with distributed training libraries (e.g., Megatron-LM, DeepSpeed, FSDP, Ray). Infrastructure : Solid understanding of CUDA, Linux environments, and cloud infrastructure (AWS, GCP, or Azure).