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As a research student , you will work on one of the teams core challenges in modern LLM-based systems, such as:

Agentic RAG
Advanced retrieval strategies, hybrid search, query rewriting, context optimization, and LLM-driven reasoning pipelines
Automatic Evaluation of Agents
LLM-as-a-Judge, scoring frameworks, hallucination detection, and robust evaluation methodologies
Agents for Knowledge Creation
Building agents that autonomously generate, refine, and maintain enterprise knowledge bases
Ontology-Grounded RAG
Enhancing models with domain-specific knowledge and structured representations
Data Analysis Agents
Agents capable of analyzing complex operational, product, and support data at scale
Fine-tuning LLMs for customer domain adaptation
Automatic agent improvement from user feedback including learning from errors, evaluations, and human signals
You will work one day per week, embedded directly in the Ask-AI research group, collaborating closely with senior researchers and engineers.

Required Technical Support Specialist - Student Position
Description
Come Join us as a member of the Technical Support Team and part of our Operations, where you will take part in facilitating and managing technical support and monitoring.
Main Responsibilities:
Being part of our customer facing team responsible for technical support and troubleshooting
Working in a 24/7 support hours' shifts
Gathering detailed technical information as needed and investigating issues to solve problems, evaluate and escalate priority issues as needed.
Spotting common trends and underlying problems
Providing reliable resolution of critical and high impact problems for customers
Updating self-help documents so customers/employees can try to fix problems themselves
Working closely with our developers and technical teams to solve customers problems efficiently and improve our product
Creatively suggest and build software work-around solutions, including occasional scripting/ coding and customizations
Part and full-time/ weekends position available.

The CPU Architect will take charge in defining a processor core that meets the requirement of high performance, high bandwidth, and scalable processing architecture. This architect will utilize his processor experience to deliver a world-class processor ASIC with many advanced features for Huawei products.

we are looking for a highly motivated student to join our Data Analytics team. This role includes exploring and extracting insights from millions of connected vehicles on the road. As a Data Analyst, you'll develop the next generation of cybersecurity and mobility data applications for the automotive industry.
This role is part-time and is Israel based.
Responsibilities:
Leading the data onboarding process of new customers.
Explore raw data of various customers as preparation for parsing and normalization in order to turn data into actionable insights and make data-driven decisions.
Use big data tools and AI to analyze large scale real-time data of connected vehicles and identify anomalies and emerging threats to create meaningful detectors that bring value to our customers.
Representing the data function both internally and externally, including customer-facing interactions.

Looking for a CPU performance modeling architect to take responsibility over the performance aspects of new CPU instructions or modes of operation. The role includes but is not limited to:
Partners with lead SW and HW architects to co-invent optimal HW and SW solutions that come to address requirements. Influences the direction based on experiments and simulation data.
Models CPU functionality, performance and/or power in pre-silicon simulators
Defines and runs performance experiments to aid feature definition. Such experiments can be performed on a pre-silicon simulation environment or in a real system or on a combination of both or even in combination with analytical models.
Provides experimental/proof of concept for new features and implementation alternatives meeting performance constraints.
Analyzes the bottlenecks of current CPUs on workloads that reflect CPU future usages.
Potentially (in the future), lead a team doing above activities.
An adequately qualified candidate can also become the leader of definition for some features in addition to all the roles above.

We are forming a new team to explore AI Compilation Boundaries
This team will operate under a unique manner, as described below -
The environment
- Startup-like intensity
Small team
High ownership
High visibility
Merit is the essence, not titles
- Fast pace
Todays state of the art is tomorrows baseline
We aim to go beyond
- Incubator mindset
Take on hard challenges, dismantle, and regroup as novelty
- Elite technical culture
Strong experts
Strong standards
Strong execution.
Ratio beats seniority, not the other way around
- No artificial boundaries
We challenge assumptions, and question constraints, and leave no quarter
- Direct exposure to the latest and greatest AI technology
Real hardware
Real toolchains
Real large-scale AI applications
All pass through and impacted by our work
- First-principles thinking
out-of-the-box is a compromise
Do not accept that a box had to exist in the first place.

Join a team developing next-generation AI accelerators, working at the intersection of AI algorithms, software, and hardware.

*Part-Time or Full-Time Opportunity

Responsibilities:
The research focuses on designing next-generation rack and SuperPod architectures that combine elasticity through resource pooling with optimal performance enabled by holistic co-design across applications, parallel programming models, interconnect fabrics, and compute, memory, and switch chip architectures. On the communication side, the work architects scalable, high-bandwidth, and low-latency scale-up fabrics across multiple chips at rack and SuperPod scale. On the memory side, it explores richer protocol semantics beyond traditional load/store operations to reduce unnecessary data movement and rethinks memory hierarchies to expose large-scale capacity with near-local latency. On the compute side, it analyzes the requirements of General Compute and Generative AI workloads and their parallel programming models to fully leverage large-scale system resources.
Investigate and prototype new architectural features, including but not limited to:
o Tiered Memory: Explore advanced memory organizations, including hardware-managed caching, to enable software-transparent, fine-grained promotion and demotion of cache lines across the memory hierarchy.
o Prefetching/Speculation: Evaluate and design existing and next-generation hardware prefetching and speculation mechanisms to effectively hide local and remote memory latencies at rack and SuperPod scale.
o Near-Memory/Network Processing: Develop support for key primitives executed at the memory and network layers to minimize unnecessary data movement across sparse, dense, and pointer-based data structures.
o Workload-Centric Co-Design: Study optimal parallelization strategies at rack and SuperPod scale for both General Compute and Generative AI workloads, and design dedicated hardware support for widely used parallel programming primitives such as RPCs and collective communication.
Write reports and papers on the research results and present them.

We are looking for a highly analytical, detail-oriented Innovation Expert to join our Open Innovation team. This role is ideal for an individual contributor operating at the intersection of deep tech, the startup ecosystem, and academia, with strong technology analytical skills, clear communication, and a foundational background in computer architecture research.

In this role, you will drive our external innovation strategy by managing academic research collaborations, scouting early-stage deep-tech startups, and conducting technical due diligence.

Key Responsibilities
Academic Cooperation & Research Partnerships: Identify and build direct collaboration agreements with world-class professors, research labs, and universities. Facilitate joint research projects, tech-transfer initiatives, and track academic spin-offs.
Startup Scouting & Ecosystem Mapping: Actively identify, source, and map early-stage deep-tech startups and emerging tech companies globally within the companys strategic domains.
Technical Due Diligence: Evaluate startup technologies, product-market fit, and team capabilities. Analyze technical validity and assess how external innovations align with our internal roadmaps.
Ecosystem Analysis: Conduct deep-dive analyses of startup ecosystems, venture capital trends, and academic spin-offs to identify high-potential investment and collaboration opportunities.
Investment & Collaboration Support: Synthesize technical and commercial findings into clear, structured scouting reports, investment tech memos, and presentations for senior leadership to drive partnership or investment decisions.
Cross-Border Collaboration: Act as a bridge between startup founders, academic innovators, and internal global teams (including European and China-based business units) to facilitate joint POCs, or investment reviews.
Track Tech Domains: AI infrastructure, advanced semiconductors, hardware-software co-design, and AI data movement.

Our team at the Huawei Computing Network Innovation Lab is looking for exceptional talent to join us and lead the development of next-generation data centers. We create cutting-edge technologies that synergize software and hardware in tandem to accelerate compute, storage, and networking at large scale. We aim to drive innovation and deliver software-defined infrastructure and algorithms for HPC, AI/ML, and Big Data applications.
We are looking for an outstanding Research Team Lead with deep hands-on expertise in large-scale distributed systems, AI framework infrastructure, and performance optimization on custom accelerators. If you are a visionary technical leader, a skilled communicator, and a team builder who thrives at the frontier of systems and AI research - you're welcome on board.

What Will You Be Doing?
Lead and grow a world-class team of researchers and engineers working on distributed AI infrastructure and systems software.
Architect and own the software infrastructure enabling distributed training and inference on Huawei's custom accelerator hardware (e.g., Ascend NPU).
Drive research and development on communication libraries, runtime systems, memory management, and graph execution & synchronization at scale.
Optimize end-to-end performance across large-scale clusters, covering both scale-up (multi-device) and scale-out (multi-node) configurations.
Design and implement high-performance communication backends and collective operations (AllReduce, AllGather, broadcast) for distributed training workloads.
Collaborate cross-functionally with hardware architects, compiler teams, and framework engineers to co-design hardware-software solutions.
Publish and present research findings at top international venues (NeurIPS, EuroSys, SC, MLSys, OSDI) and represent the team externally.
Mentor engineers and researchers, conduct performance and growth reviews, and shape team culture and technical direction.
Partner with top academic institutions and open-source communities to advance the state of the art in distributed AI systems.

*Part-Time or Full-Time Opportunity

In this job opening, we are seeking a highly motivated and visionary researcher to join our team focused on chiplet-based computing systems. This role involves research and development in chiplet-based architectures, aiming to overcome performance and scalability limits of traditional compute platforms.

Responsibilities:
Conduct cutting-edge research on the co-design of communication and computation for wafer-scale systems. On the communication side, the work focuses on architecting Networks-on-Chip, providing scalable, high-bandwidth, and ultra-low-latency interconnects that efficiently span multiple dies and support fine-grained data movement. On the computation side, the research emphasizes optimizing and mapping training and inference workloads to fully exploit wafer-scale resources, improving performance, scalability, and energy efficiency through intelligent task placement and scheduling.
Investigate various aspects of system design, including:
Network-on-Chip design, further including Topology exploration, routing algorithms, and protocol/flow-control design to support high-throughput collective communication, low-latency synchronization, and efficient model/data parallelism
Expressing and Mapping LLM Parallelism to Wafer-Scale System that simplify LLM parallelism (e.g., tensor, pipeline, and data parallelism) and efficiently map training and inference workloads onto wafer-scale system

We are looking for an architect to work on the invention and development of new SW visible CPU technologies to improve performance of SW scenarios related to memory ordering, memory coherency, multithreading, synchronization, memory safety, caching, etc.
The minimal tasks of this role are:
1. Modeling of new memory related architectural features in the existing CPU performance simulator
2. Development of performance tests or adaptation of existing workloads to be suitable for simulation
3. Conducting performance studies for the workloads of choice either in the performance simulator or on a real chip
4. Develop analysis, conclusions and recommendations based on the above activities
The role can comprehend much more than that depending on the skills, talent and experience of the candidate creating a career path over time. For example, to invent new instructions and mechanisms to overcome significant performance bottlenecks in these areas, refinement and further development of ideas provided by others. Possible participation in development in additional domains
If you want to be part of something bigger, if you are a team player with excellent communication skills and motivation to revolutionize data-center technology, youre welcome on board!

We are looking for a Principal Engineer with deep expertise in database internals, system architecture, and hardware-aware performance optimization.
This role is responsible for defining and driving advanced database performance solutions by understanding and improving the interaction between database engines, operating systems, and modern hardware architectures.
The ideal candidate is an experienced technical leader with a strong background in database systems, performance engineering, and computer architecture. The candidate should be capable of leading complex technical initiatives, influencing architectural decisions, and developing innovative solutions for next-generation database platforms.
This is a hands-on technical leadership role requiring expertise across database kernels, query execution, transaction processing, memory systems, and low-level performance optimization.

Responsibilities
Define and drive technical direction for database performance optimization and hardware-aware database architecture.
Lead the design and implementation of advanced database optimizations focused on scalability, efficiency, and workload performance.
Solve complex performance challenges across the full system stack, including database engines, query execution, transaction processing, concurrency control, storage engines, operating systems, and hardware platforms.
Design and develop optimizations leveraging modern hardware architectures, including multi-core CPUs, large-memory systems, NUMA platforms, CXL memory, RDMA networking, and advanced storage technologies.
Improve database execution engines, caching mechanisms, memory management, storage systems, and workload scheduling.
Analyze system behavior and identify bottlenecks using profiling, tracing, hardware performance counters, and low-level performance analysis.
Develop benchmarks, workload models, and methodologies to evaluate scalability, efficiency, and architectural tradeoffs.
Collaborate with database kernel, infrastructure, and engineering teams to influence architecture decisions and deliver high-impact improvements.
Mentor senior engineers and contribute to building technical expertise across the organization.
Drive innovation through research, prototyping, technical analysis, and architectural improvements.