Machine Learning Engineer Intern

Machine Learning Engineer Intern Commitment Period: Jan 2027 onwards (minimum 6 months, full time) Job Description: We are looking for a ML Engineer Intern to research and build methods that find best compressed neural networks. Focus of this work includes knowledge distillation (KD) and quantization-aware distillation (QAD). Deliverable: a comparison of techniques by model family, with reported recommendations. Responsibilities: Develop KD and QAD approaches across different model types Ablate key choices among various techniques Build reproducible training and evaluation scripts, and report findings Techniques You Will Work On Knowledge distillation: soft-label, feature-based and self-distillation Quantization-aware training (QAT) and QAD Low-bit quantization with post-training quantization (PTQ) as the baseline Requirements: Currently studying CS, Engineering, AI/ML or a related field Commitment for at least 6 months full time Strong Python and hands-on PyTorch (custom training loops and losses) Trained and evaluated vision models end to end Knowledge of KD and quantization basics (temperature, PTQ vs QAT) Careful, reproducible experiments and clear written reports Preferred (Plus): Project or thesis on model compression (quantization) Experience with PyTorch quantization tools or ONNX export Publications at leading conferences (i.e. CVPR, ICML, NeurIPS etc.)