Algorithm Engineer Intern

Algorithm Engineer Intern Commitment Period: Jan 2027 onwards (minimum 6 months, full time) Job Description: We are looking for an Algorithm Engineer Intern to build search methods that find best compressed neural networks. Focus of this work includes neural architecture search (NAS) and on reinforcement learning (RL) for neural networks. Deliverable: reusable NAS tooling, and a comparison of RL against simple baselines for mixed precision. Responsibilities: Enable common NAS patterns: search spaces and search strategies in reusable tooling Explore RL for mixed precision: per-layer bit-width selection, compared against simple baselines Build evaluation scripts with simple latency and size proxies, and report findings Techniques You Will Work On: Common NAS techniques like search-space design RL: policy-gradient and actor-critic methods, reward design Mixed-precision quantization Minimum 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 Trained and evaluated vision models end to end Knowledge of RL basics (MDPs, policy gradients) Knowledge of quantization basics Preferred (Plus): Hands-on NAS or AutoML Experience with Computer Vision models Publication or thesis in AutoML, RL or model compression Publications at leading conferences (i.e. CVPR, ICML, NeurIPS etc.)