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).