Knowledge distillation jobs Skill: Knowledge distillation Ingénieur de Recherche Vol de modèle par extraction
Thales Group · Palaiseau, FranceInternship · Intern
AI Research Scientist: Multimodal Foundation Models - Architecture, Pre-Training & Distillation
Apple · Seattle, US+1 more On-site
Research Intern — Neural Network Training and Hardware–Software Co-Design
Meta · Burlingame, USOn-site · Internship · Intern
Principal Perception Engineer
Magna International · Shanghai, ChinaFull-time · Principal
+10K new in the last hour
Thales Group · Posted Nov 12, 2026
Stage de fin d’études de 6 mois en recherche sur la sécurité des modèles d’IA, incluant l’étude des attaques par extraction et des mécanismes de défense.
Tous nos stages sont conventionnés et soumis à une gratification dont le montant est déterminé selon votre niveau d’études.
Étudier les méthodes d’attaque par extraction de modèles Analyser les mécanismes de défense contre le vol de modèles Implémenter et comparer ces méthodes Proposer des améliorations Rédiger la documentation et publier les résultats Master's in Data Science or Machine Learning Apple · Posted Nov 10, 2026
Lead research on multimodal foundation models, designing architectures, pre‑training, and distillation for Apple products.
Seattle, Washington, United States Sunnyvale, California, United States Investigate fundamental questions about multimodal foundation model design, training, and distillation Develop and evaluate new model architectures, pre‑training objectives, data strategies, and optimization methods Co‑develop frontier models and efficient models for Apple silicon and on‑device intelligence Design architectures that balance memory, latency, energy, and privacy requirements Translate research into foundation‑model technologies for Apple products Master’s degree in machine learning, computer science, or related field Nice to have: open‑source contributions Nice to have: publication record Meta · Posted Nov 10, 2026
Internship focused on designing and training neural networks for efficient execution on custom hardware for wearable and robotics devices.
Design, train, and evaluate neural networks for on-device workloads under compute, memory, and energy constraints Develop hardware-aware training and model compression techniques and measure their impact on target hardware Explore partitioning models across multiple accelerators or chips, including how inter-chip communication affects accuracy and performance Profile models end to end and use the measurements to drive both model and hardware design decisions Prototype and validate on FPGA platforms, modifying and debugging existing RTL as needed PhD in Computer Science, Electrical/Computer Engineering, Machine Learning, or a related field Magna International · Posted Aug 20, 2026
Responsible for deploying and optimizing perception models for autonomous driving on embedded platforms, ensuring real-time performance and accuracy, and achieving high-performance edge inference.
Deploying and optimizing perception models for autonomous driving on embedded platforms Participating in model optimization pipeline (conversion, quantization, pruning, knowledge distillation) Analyzing on-device resource performance (CPU / NPU / DSP / GPU) and providing optimization suggestions Bachelor’s degree or above in Computer Science, Automation, Electronic Engineering, or related fields Nice to have: Mass production project experience
Nice to have: large‑scale training experience
Nice to have: multimodal model expertise
AI Research Scientist: Multimodal Foundation Models - Architecture, Pre-Training & DistillationApple · Seattle, US+1 more
Research Intern — Neural Network Training and Hardware–Software Co-DesignMeta · Burlingame, US