Model Implementation & Benchmarking: Reproduce, adapt, and benchmark state-of-the-art architectures from recent AI research papers.
Data Engineering: Curate, preprocess, and augment large-scale datasets for model training, fine-tuning, and evaluation.
Experimentation & Fine-Tuning: Assist in training and fine-tuning models (e.g., LLMs, Diffusers, or Vision models) using techniques like LoRA, QLoRA, or RLHF.
Evaluation & Alignment: Design and implement rigorous evaluation frameworks to measure model performance, bias, hallucination rates, and safety.
Pipeline Optimization: Help optimize training loops and inference speeds using tools like DeepSpeed, TensorRT, or vLLM.
Requirements:
Degree/Higher Diploma in Computer Science / Information Technology or equivalent
Experience in Python or Java or .Net or PhP or javascript
Self-motivated, able to work independently and willing to learn