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.)