Intel

Internship role focused on applied research and productization of Vision-Language Models (VLM) and Vision-Language-Action (VLA) models, including pre-training, fine-tuning, alignment, data pipelines, fusion strategies, action components, and model optimization for efficient deployment on Intel hardware. The role involves evaluating models and potentially publishing results.

AI Algorithm Research Intern focused on developing, implementing, and benchmarking algorithms for Intel's next-generation neuromorphic architecture to enable applications in edge computing, signal processing, and autonomous systems. The role involves contributing to Intel's neuromorphic SDK and publishing research findings.

Intern position at Intel's Neuromorphic Computing Lab focused on developing, implementing, and benchmarking algorithms for next-generation neuromorphic architectures. The role involves supporting application development, publishing research, and contributing to the neuromorphic SDK, with a focus on edge computing, signal processing, and autonomous systems.

AI Algorithm Engineer Scientist at Intel focused on generative AI, specifically for building next-generation code generation agents for GPU programming. The role involves research and development of ML models, algorithm optimization for CPUs/GPUs, and translating models into deployable products, with a focus on areas like audio, voice, speech, and vision processing.

Internship role focused on advancing compiler infrastructure for heterogeneous AI workloads by developing novel optimization techniques for AI kernel compilation targeting both CPU and GPU architectures using MLIR/LLVM. Explores algebraic optimization, hierarchical scheduling, and cost-driven pruning for high-performance fused kernels.

Senior Principal Engineer in AI Applied Research at Intel, focusing on applying AI/ML to logic IP design and semiconductor manufacturing. The role involves conducting applied research, developing proof-of-concept models, and implementing solutions to demonstrate business value, requiring expertise in deep learning, ML, RL, NLP, GNNs, and time-series. The position emphasizes leadership, influencing partners, and mentoring technical leaders.

Robotics Research Intern at Intel focusing on advanced algorithmic development and robotics research for next-generation robotic technologies. The role involves researching, designing, and optimizing robotics algorithms, control systems, and AI/ML models, with a focus on enabling intelligent autonomous systems and innovative robotic applications. Collaboration with cross-functional teams to translate research into practical implementations is key.

Research intern position focused on Confidential AI systems and Agent-CC solutions for hyperscale, involving LLMs, AI systems, and microarchitecture. Requires programming, foundational computer architecture, and AI math skills.

Systems Research Engineer/Scientist role focused on leveraging AI/ML for higher efficiency and performance in system architecture innovations, including high-performance cluster computing, virtualization, and accelerated computing. The role involves prototyping, characterizing, and analyzing workloads, developing tools for performance assessment, and influencing future product roadmaps. Requires strong systems knowledge and hands-on experience with AI workloads, with a focus on performance modeling and analysis of AI inference or training.

Research intern focusing on system reliability (RAS) and silent data error characterization and mitigation for AI and general-purpose compute platforms, including heterogeneous systems and large-scale server clusters. Responsibilities include designing and running experiments, analyzing logs, and prototyping detection/diagnosis methods to improve data integrity and platform robustness across the HW/FW/OS/runtime stack.

Research Intern focusing on system innovation, cost optimization, and GPU interconnect protocols for disaggregated AI supernode architectures. The role involves exploring architectural innovations, implementing distributed memory pooling, and researching Ethernet-native GPU interconnect protocols for large-scale AI inference and training clusters. Familiarity with RDMA, Mellanox tools, and LLM inference benchmarking methodologies is required.

Memory Research Scientist at Intel in Tokyo, Japan, focusing on AI/HPC, compute architecture, memory technology, and advanced system architecture. The role involves leading technical engagement for the ZAM NEDO program, coordinating with partners and US-based teams, and driving applied research for Japanese government/commercial initiatives. Requires deep experience in semiconductors, full development lifecycle, and strong technical coordination between US and Japanese teams.

This role is for a PhD-level Quantum Device Measurement Engineer focused on characterizing silicon spin qubits at millikelvin temperatures using electrical measurements and quantum circuit computations. The position involves designing experiments, analyzing device performance, and collaborating to advance Intel's quantum computing roadmap, operating within a research environment focused on semiconductor-based quantum systems.