דרושים » תוכנה » Software Engineer, AI Networking Architect

We are seeking an AI Networking Exploration Architect for our Networking Insights Group to bridge the gap between cutting-edge, hyper-scale AI workloads and the datacenter infrastructure that enables them. You will join a small, focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding and end-to-end systems thinking. Your insights will directly shape our products across the full stack-from applications and software libraries to hardware architecture and physical design.

What You'll Be Doing:

Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.

Translate state-of-the-art research into actionable infrastructure, software, and hardware features in partnership with architecture teams.

Rapidly master new AI domains (LLMs, generative models, multimodal systems) and distill key findings for product teams.

Incorporate your deep knowledge of AI applications into our hardware and software roadmaps.

Conduct independent research by formulating hypotheses about workload behavior and validating them through rigorous analysis.

Drive architectural innovation and network optimization by applying your domain expertise to exploratory analysis of real-world Deep Learning (DL) workloads.

In this role, you will help build the evolution of our DOCA Networking software stack - the accelerated infrastructure framework powering AI factories and distributed computing platforms. You will drive software innovation from vision to real-world impact, influencing some of the most advanced computing systems in the world. As part of the DOCA Product Group, you will lead software strategy for ConnectX NIC and BlueField DPU platforms - key pillars of our data center and AI networking strategy - helping build the intelligent infrastructure of tomorrow.

What You'll Be Doing:

Lead the Product-strategy for DOCA networking stack and products across their life-cycle: from vision and inception, through detailed customer & ecosystem requirements, roadmap crafting, market introduction, growing into in-scale delivery, and product improvement cycles.

Orchestrate a unified technical strategy between AI product teams, engineering teams, and customers to advocate the use of DOCA libraries & microservices, and to develop new DOCA APIs and services for new deployments.

Drive multidisciplinary engineering and architecture teams to establish priorities and define precise, actionable requirements for breakthrough projects.

Forge strong partnerships with customers and ecosystem partners - actively listening to their technical needs, delivering expert mentorship, and supporting successful, large-scale AI (and other) deployments that drive their strategic goals.

Create use cases and reference applications to demonstrate product value to technical and executive audiences.

Gather insights to define future products, including analysis of complementary and competitive products and customer feedback.

As part of our engineering organization, you will play a key hands-on role in developing and executing software-driven characterization workflows on NVIDIA rack-scale systems. This role is focused on running AI workloads across the full stack to analyze, characterize, and optimize power, performance, and drive behavior at system level. This is an opportunity to work at the intersection of software, infrastructure, silicon, and large-scale AI platforms, with direct impact on our next-generation systems.

What youll be doing:

Develop and run software tools, automation, and workloads to characterize power, performance, and drive behavior across our rack-scale systems.

Execute AI and system-level workloads to stress and evaluate behavior across the stack, including GPUs, CPUs, networking, storage, firmware, drivers, and system software.

Build automated frameworks for data collection, telemetry, validation, correlation, and analysis of characterization results.

Investigate system behavior under different workloads and operating conditions to identify bottlenecks, anomalies, and optimization opportunities.

Work closely with hardware, firmware, driver, system software, performance, and validation teams to define characterization methodologies and debug cross-stack issues.

Support bring-up, validation, and readiness activities for new rack-scale platforms and AI infrastructure.

Create clear documentation, test flows, and repeatable processes to improve coverage, efficiency, and reproducibility.

Required Machine Learning Hardware Architect, Hardware, Software Co-Design, Cloud
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Tel Aviv, Israel; Haifa, Israel.
About the job
In this role, youll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers our most demanding AI/ML applications. Youll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.
As a Machine Learning Hardware Architect within the Co-design team, you will serve as a technical lead bridging model architecture innovation and next-generation hardware design. Operating at the highest levels of AI research and engineering, you will define the goal and architectural roadmap for our future machine learning serving and training capabilities. You will guide the integration of ML research such as massive-scale foundation models with advanced silicon architectures to create industry-leading, high-performance, and power-efficient accelerators.
Responsibilities
Define and drive the technical roadmap and architecture for the hardware/software stack to ensure exceptional performance for ML models. Act as the technical liaison across research, software, and hardware teams, steering model architecture innovation to maximize scaling, quality, and hardware efficiency.
Architect next-generation configurable simulation frameworks and performance models, setting the organizational standard for evaluating complex microarchitectural decisions. Drive high-stakes choices regarding Power, Performance, Area (PPA) and buildability for future chip and system architectures, expertly balancing long-term technological trends with strict product delivery timelines.
Guide system-level performance analysis across highly distributed ML systems, innovating new methodologies to optimize and balance compute, memory bandwidth, and inter-chip network requirements. Their leadership will directly shape the future of high-performance AI infrastructure and hardware-software co-design.
Manage cross-functional partnerships across hardware, compiler development and ML teams.

We're looking for a Senior Data Scientist to join the AI cybersecurity team in the Security and Networking Architecture group. As a Senior Data Scientist youll have the opportunity to take an active part in the research and development of our world-class networking and data center security products. This role involves creative problem solving alongside engineering teams, and is key for the continued success of AI networking security.

What youll be doing:

Developing agentic AI systems for security, combining generative models, RAG, and tool-augmented reasoning to automate threat analysis and response workflows.

Optimizing and fine-tuning models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.

Developing, implementing and improving models and algorithms across media types, whether time series, images, text, audio or video.

Leveraging data pipelines to efficiently process and transform large volumes of data for training and inference purposes.

Applying alignment techniques and parameter efficient fine-tuning to improve model performance.

Measuring and benchmarking model and application performance to drive improvements.

Driving the gathering, building, and annotation of domain specific datasets for benchmarking and training.

Collaborating closely with software and hardware engineers on new features and improvements. Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.

We're looking for a Senior AI Infrastructure Engineer to join a group that specializes in Security and Networking, and specifically ML/AI, MLOps, and agentic AI development. As a Senior AI Infrastructure Engineer, youll build and maintain the infrastructure, tools and processes necessary to support the AI lifecycle in a production environment. You will collaborate closely with data scientists, software engineers, and security architects to ensure smooth development, deployment, evaluation, and optimization of AI pipelines, models, and agents. This role requires a balance of high-level engineering rigor and a collaborative spirit; youll be a technical anchor and a supportive peer for teams across the organization.



What youll be doing:

Architecting, developing and optimizing scalable infrastructure for deploying security and networking AI models and agents in production.

Managing ML/agentic workflows to ensure performance, high availability, resource efficiency, and cost-effectiveness.

Designing and implementing pipelines and frameworks for AI training, inference, and experimentation.

Partnering with data scientists and security architects to operationalize AI agents, including packaging and integration with existing systems. This includes contributing to and reviewing code, design documents, and test plans.

Partnering with DevOps teams to integrate pipelines and workflows into CI/CD processes, ensuring reliable deployments and rollbacks.

Building proactive monitoring systems to identify issues in quality and infrastructure before they impact production.

Implementing access controls, authentication mechanisms, and encryption standards to keep our AI models and data secure.

Documenting guidelines and leading knowledge-sharing sessions to elevate the teams collective development expertise.

we are seeking a Hands-on Architect to design, build, and evolve the core of next-generation AI cybersecurity platform.This is not an ivory-tower role; it is for a builder at heart. You will write code, build functional prototypes, and own critical services from inception to production. This role demands a unique blend of deep, code-level execution with a broad, system-wide architectural vision that spans our entire data, cloud, and AI stack.
Responsibilities:
Rapidly code and build functional proofs of concept (PoCs) and prototypes to explore and validate new architectures, data platforms, data processing frameworks, and GenAI capabilities.
Validate technical feasibility, scalability, and business impact through working software, not just diagrams or documents.
Partner closely with engineering, product and data science teams to translate emerging technologies into production-ready, scalable systems.
Lead architecture design reviews, proactively identify scalability and performance bottlenecks, and embed security principles into the design process from day one.
Serve as a technical leader and mentor, elevating the team's skills through pair programming, in-depth code reviews, and deep-dive sessions on system design and software craftsmanship.

As a Senior AI Engineer in the global CTO group, you will play a central role in building the next generation of AI-powered security capabilities across our product portfolio. This role is focused on rapid prototyping, experimentation, and innovation, turning emerging ideas into working product features that can scale across multiple products and technology stacks.

You will design and build AI-driven systems end-to-end, from agent-based workflows and model integrations to backend services, data pipelines, and product-facing capabilities. You will work closely with product, engineering, and research teams across the company to explore new use cases, validate ideas quickly, and bring impactful AI features into production.

This role is ideal for an experienced AI engineer who enjoys moving fast, working across boundaries, and building real production systems, not just experiments. Your work will directly influence how AI is embedded across our platforms and how customers experience secure AI at enterprise scale.

The NVIDIA Networking Advanced Development Software team develops new groundbreaking technologies to enable new market shares for the company and tighten customer relationships. These are emerging technologies in networking and distributed computing for the booming AI factories and data centers. They span areas such as AI neural networks, Deep Learning, High Performance Computing (HPC), Storage, Cloud, SW Defined Network, Network Function Virtualization and more. We develop the solutions top-down, all the way from application behavioral analysis, to architecture definition and down to the implementation, using the world-leading NVIDIA devices. The development traverses any needed component - application SW, middleware SW, OS kernel subsystems, device drivers, embedded SW (Firmware) and CUDA GPU. We collaborate with partners and key customers in the analysis processes and engage with open source communities introducing our leading features.

What youll be doing:

Design and implement solutions throughout all layers from high level application, OS and driver subsystem to firmware.

Work on impactful projects involving state-of-the-art high-performance computing hardware and software.

Provide insight and technical guidance and collaborate with peers from across the company - including software architecture, chip architecture, and engineering departments to improve our future technology.

Collaborate with NVIDIA partners and customers.

We are looking for a Senior Software Engineer to join the AIOps platform team and help build the core distributed systems that ingest massive telemetry streams from GPU clusters and operationalize predictive AI models at scale. You will work at the intersection of high-performance data engineering and production ML, turning research algorithms into reliable, mission-critical software.

What you'll be doing:

Architect and build an agentic AIOps system that autonomously monitors GPU fleet health, aggregates and correlates massive telemetry streams, surfaces intelligent alerts, and orchestrates multi-step diagnostic workflows and corrective actions - powering real-time dashboards, automated root-cause analysis, and proactive incident response.

Research, evaluate, and prototype data storage strategies and data representations across diverse database technologies and modalities, ensuring AI models are trained on high-quality, well-structured data that improves predictive accuracy and generalization.

High-Scale Engineering: Design distributed systems to handle the extreme telemetry density of large-scale AI clusters, ensuring efficient data ingestion, processing, and real-time analysis.

Instrument services with deep observability (metrics, logs, traces) to support rapid debugging and continuous performance improvement.

Build and own the model-serving infrastructure that operationalizes predictive algorithms at scale - packaging, versioning, deploying, and monitoring AI models in both SaaS and on-premises environments.

Contribute to the platform's core libraries and abstractions that accelerate development across the broader AIOps engineering team.