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.

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.

Are you passionate about systems programming at the intersection of hardware and software? Do you want your code to directly accelerate the world's largest AI training and inference workloads? The Elastic Fabric Adapter (EFA) Drivers team is looking for Software Development Engineers to design and implement the kernel drivers and userspace libraries that power high-performance networking for AWS's AI/ML and HPC infrastructure.

EFA is the custom network interface that enables thousands of GPUs and accelerators to communicate at near-wire-speed bandwidth and low latency, making it a critical enabler of both foundation model training and real-time inference serving at unprecedented scale. When customers train the next generation of large language models or serve billions of inference requests with tight latency SLAs on our EC2 P5, P6, or Trn instances, it's our driver stack that moves the data.

Key job responsibilities
- Design, develop, and optimize Linux kernel drivers (RDMA/EFA) and userspace provider libraries (rdma-core) that ship to millions of EC2 instances.
- Work directly with custom hardware - collaborate with chip designers to bring new silicon capabilities to life in software.
- Contribute to the upstream Linux kernel RDMA subsystem and rdma-core open-source project.
- Architect solutions for next-generation networking features: GPU-direct RDMA, adaptive routing, collective offloads, and multi-path transport.
- Build monitoring and automation tools to enhance driver testing for functionality, reliability, and performance.
- Own the full lifecycle: from initial hardware bring-up and feature enablement through testing, deployment, and production troubleshooting across the global AWS fleet.

In this role, you will be responsible for several teams of architects, engineers and software developers, all working together to conduct state-of-the-art R&D in system and network architecture. As the group lead, you will guide and mentor the individual team leads, and also conduct hands-on work leading architecture, technology innovation and technical planning and of high-performance computing cluster network, which oriented at AI, HPC, and big data.

Responsibilities
You will perform a wide range of duties including:
Architecture Innovation:
Deeply analyzing the advantages and disadvantages of mainstream network systems, to find opportunities for network architecture innovation;
Insight into the technology developing trend of the high-performance computing network field, and leading the corresponding technology planning.
Exploring new architectures of high-performance computing network systems and efficiently integrating communication library, topology, and network protocol to solve performance bottlenecks.
Technical breakthroughs in networking and cluster routing algorithm：
Analyzes computing cluster network performance and leads the development of computing cluster network technologies
Research and optimize the heterogeneous interconnection topology of key computing chips to continuously improve the key competitiveness of Huawei computing heterogeneous chipsets.
Responsible for the research of data center network technologies, and guide network topology design and routing algorithm development
Group leadership:
Lead the development of a comprehensive system architecture for AI Fabric and HPC Fabric solutions.
Manage and mentor highly skilled team leaders, to ensure that the group operates together in pursuit of common goal.
Foster a collaborative and innovative work environment.
Provide technical guidance and support to team members.
Collaborate closely with cross-functional teams internationally, including hardware, software, and ucode design teams, to ensure alignment of architectural decisions with product and platform common objectives.
Initiate and supervise collaborations with top academic researchers in Israel and abroad.
Stay up to date with emerging technologies and industry trends in AI, HPC and big data industries.
Evaluate and recommend technologies and next generation projects.
Occasional travel related to ongoing projects, seminars, conferences etc.

We are looking for an experienced Software Team Leader to build and lead a new team focused on AI and agent technologies for enterprise data platforms.
This is a hands-on leadership role. The Team Leader will define the technical direction, build the first solutions, recruit engineers, and turn new AI ideas into production systems.
The main focus is AI agents, RAG, natural-language interaction, model integration, and intelligent workflows. Database technologies are an important foundation, but not the center of the role.

Responsibilities
Lead a new software engineering team.
Define the AI architecture, roadmap, and technical priorities.
Build AI agents that reason, use tools, and execute multi-step workflows.
Develop agent orchestration, state management, and workflow execution.
Build agent workflows for data analysis, automation, and decision support.
Integrate LLMs, embedding models, and model-serving platforms.
Develop tool and API integration for AI agents, including MCP where relevant.
Optimize AI systems for latency, throughput, cost, and scale.
Move successful prototypes into production-grade software.
Work closely with data, database, infrastructure, cloud, and product teams.

As a leading technology innovator, we push the boundaries of what's possible to enable next-generation experiences and drive digital transformation, creating a smarter, connected future for all.

As a company AI Software Engineer, you will develop and implement cutting-edge AI techniques that enable the efficient utilization of state-of-the-art solutions across various technology verticals.

In this position, you will be responsible for leading software development and implementation of the company AI Stack features within our AI Runtime (QAIRT) SDK, specifically Core software including Generative AI Inference Extensions. You will contribute to the efficient execution of advanced deep neural networks (DNNs), large language models (LLMs), and other modern AI architectures. You will also be responsible for supporting AI stack that optimizes runtime efficiency, latency and power consumption of AI applications running Machine Learning workloads on our low-power AI accelerators. The DSP core design work directly supports our NPU and GPU solutions, enabling optimized inference across diverse hardware platforms.

You will have the opportunity to demonstrate your passion for software design and development through your analytical, design, programming, and debugging skills.
Responsibilities
Develop real-time software for our AI DSPs to support the execution of the latest generative AI models on Snapdragon platforms

Develop and implement core components of our AI Stack runtime framework for inference on resource constrained AI systems, operating in low power, small memory footprint platforms

Validate, analyze, and optimize the performance and accuracy of software through detailed testing of machine learning use cases

Debug complex issues, perform root cause analysis, and ensure high system reliability

Collaborate with cross-functional teams to deliver robust, scalable AI software solutions

Contribute to a culture of technical excellence, knowledge sharing, and continuous improvement within the AI Software team

Participate in design and code reviews

Working in global environment and collaborating inside the team and across other AI teams

We are looking for an experienced Tech Lead to lead a team of 3-5 software engineers developing innovative database security and data protection solutions for enterprise platforms.

This is a hands-on technical leadership role focused on cybersecurity, data security, and advanced software engineering. The successful candidate will combine deep security expertise with strong software architecture skills to define technical direction, lead engineering execution, mentor developers, and build enterprise-grade security solutions.

The role requires strong technical ownership, the ability to solve complex security challenges, and close collaboration with security researchers, product teams, customers, and engineering organizations. Occasional travel may be required for technical discussions and customer engagements.

Responsibilities
Lead and mentor a team of 3-5 software engineers, providing technical guidance and driving engineering excellence.
Define software architecture, technical strategy, and development direction for database security products.
Design and develop advanced cybersecurity and data security solutions for enterprise environments.
Lead security architecture discussions, technical design reviews, and code reviews.
Translate complex security requirements and research concepts into practical, scalable software solutions.
Drive secure software development practices across architecture, design, implementation, and deployment.
Solve complex technical challenges related to security, reliability, scalability, and performance.
Collaborate with security researchers, product management, and cross-functional engineering teams.
Participate in customer-facing technical discussions and occasional international travel.
Remain hands-on and contribute to critical software components and technical decisions.

We are seeking a Senior Software Development Engineer to design, develop, and optimize mission-critical embedded software for cloud infrastructure. You will join teams focused on networking, machine learning acceleration, and high-performance computing (HPC), impacting millions of AWS services globally.

*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

*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.