דרושים » מערכות מידע » בוגרי מדעי המחשב? מסלול הקריירה של אינפיניטי יכניס אתכם לתעשייה!

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.

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.

We are looking for Software Automation Engineer to help us develop a semiconductor platform based on a revolutionary architecture.

Take part in the development of products within a disruptive system architecture. Youll have the opportunity to work in a dynamic, open, fast-paced environment, developing the technologies that power the worlds largest cloud provider. AWS provides a highly reliable, scalable, low-cost infrastructure platform in the cloud, which powers hundreds of thousands of businesses in 190 countries around the world.

Key job responsibilities
As Software Automation Engineer, you'll be responsible for creating ML (Machine learning) and HPC (High performance computing) automation and tools, create CI/CD flows and implement effective dashboards with high scale.

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.

We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying our AI models. You will own and evolve our benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.

This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models.

Location: Ramat Gan, Israel (hybrid model)

What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.