Head - AI Research

Company Overview

Position Overview

Responsibilities

• Define the multi-year research roadmap for personal LLMs, aligning it with Samsung's device-intelligence vision and the evolving PersonalLLM agenda.
• Own end-to-end delivery of research objectives from problem framing and hypothesis design through prototyping, on-device validation, and production hand-off.
• Bridge the AS-IS → TO-BE transition: move the organization beyond distillation/pruning/quantization, RAG, and LoRA fine-tuning toward dynamic compression, semantic memory compression, neural prompt compression, learned KV-cache eviction, adaptive decoding, end-to-end agentic models with self-reflection/self-verification, private continual learning, and energy-aware inference planning.
• Establish research best practices: reproducibility, evaluation harnesses, privacy benchmarks, and on-device profiling standards.
• Represent SRIB in external research communities, open-source collaborations, and academic partnerships; build a pipeline of talent through mentoring and university engagement.
• Contribute to IP strategy by identifying patentable inventions and prior art.

Skills & Experience

• PhD/ Masters in a relevant AI field (e.g., Computer Science, Machine Learning, Artificial Intelligence, Natural Language Processing, or a closely related discipline).
• Experience
• Minimum 10 years of experience in a relevant research area (LLMs, on-device/edge AI, reinforcement learning, continual learning, privacy-preserving ML, or federated learning).
• Technical Skills
• Languages & Scripting: Python (expert), C/C++ (intermediate+), shell scripting; familiarity with on-device/mobile development (Java/Kotlin or Swift) is a plus.
• Deep-Learning & LLM Frameworks: PyTorch (expert), JAX (intermediate); Hugging Face Transformers, PEFT/LoRA, DeepSpeed, vLLM, TGI, TensorRT-LLM, ONNX Runtime, ExecuTorch, MLC-LLM, llama.cpp, MNN/TFLite.
• Model Compression & Efficient Inference: Distillation, pruning, quantization (INT8/INT4, weight-only, activation-aware), LoRA/QLoRA; dynamic compression, adaptive precision, task-specific extraction. KV-cache optimization (quantization, offloading, paging, learned eviction, semantic retention); FlashAttention, speculative decoding, early exit, adaptive decoding, hardware-aware scheduling.
• Context, Prompt & Memory: RAG, summarization, sliding-window context; semantic memory compression, neural memory tokens, neural prompt compression, latent intent representation.
• Reinforcement Learning & Test-Time Scaling: RLHF/RLAIF, PPO/DPO, policy-gradient methods, reward modeling; test-time compute scaling, adaptive inference controllers, cost-aware reasoning frameworks, personalized inference schedulers.
• Personalization, Continual & Privacy-Preserving Learning: Continual/lifelong learning, catastrophic-forgetting mitigation, private continual learning, lifelong memory; differential privacy, federated learning, secure aggregation.