לוח משרות דרושים הייטק AI , לחיפוש זה נמצאו 313 משרות

Our team is looking for a skilled professional to join the AI software group. In this role, you will focus on our AI kernel compilers, ensuring our hardware delivers peak optimization for sophisticated and custom AI kernels. You will work extensively with various kernel infrastructures, including Triton and CUDA, while collaborating closely with our AI graph compiler teams.



Responsibilities
Develop our AI kernel compilers and related components in the SW stack.
Drive the performance optimization of AI models running on our unique architecture.

Are you an AI Analyst ready to translate raw model output into a reliable, high-quality production system? Do you excel at the craft of Prompt Engineering and thrive on the challenge of ensuring Generative AI output meets rigorous quality standards in mission-critical applications?
Join our R&D Operations Team. You will be the AI analyst responsible for the end-to-end quality, performance, and operational tuning of our Generative AI-driven support system (Co-Pilot). Your mission is to actively shape the model's intelligence, govern the data it uses, and implement the mechanisms that guarantee its accuracy, directly accelerating our customer support engineering velocity.
Your Impact
Support & Customer Advocacy: Champion the Support Journey in Engineering. Develop in-product support instructions that reflect and address real incoming cases, enhancing the quality and effectiveness of AI feature solutions.
Model Quality Validation: Use existing evaluation platforms and methodologies to validate production models. Monitor quality metrics to continuously assess and rank AI answers for accuracy and reliability.
Prompt Engineering & CT Loop: Drive the Continuous Training loop through systematic prompt engineering (refining and versioning inputs). Analyze failures to define R&D actions or features needed to close model performance gaps.
AI Knowledge Governance: Act as AI Content Governor, implementing controls to verify and ingest compliant knowledge base content, ensuring a quality data source.
Cross-Functional SME: Serve as the AI Support Co-pilot Subject Matter Expert, partnering with R&D and Support Enablement to translate quality issues into core model logic improvements and feature development.

The Cortex Content team powers our companys security operations platforms (XSIAM/XSOAR) by building the essential integrations, data connectors, and intelligence layers that protect our customers.
We are looking for a Senior Software Engineer who is passionate about AI and automation to help scale our content ecosystem. In this role, your focus will be leveraging AI to accelerate the delivery, quality, and quantity of our security integrations and content infrastructure. You will design and develop intelligent tools and workflows that enable engineering peers and Technical Program Managers (TPMs) to scale their output, collaborating with them as they test, adopt, and integrate these solutions into our development lifecycle.
Your Impact:
Scale Content Delivery: Design and implement AI-driven systems that empower the team to significantly increase both the quantity and the rigorous quality of our security integrations.
Develop Cross-Functional Tooling: Build internal developer tools and agentic workflows that enable other engineers and TPMs to streamline content creation, validation, and management.
Optimize Workflows: Create reliable CI/CD pipelines incorporating AI agents to automate code generation and testing processes.
Support Team Adoption: Collaborate with engineering peers and cross-functional partners as they test and integrate your tools into their daily workflows, using their feedback to drive continuous improvements.
Contribute to AI Integration: Help lead the practical integration of LLMs and automation frameworks into our day-to-day engineering processes.

As a Principal AI Software Engineer on our Generative AI foundation team, you will build AI agents and shared infrastructure that power GenAI capabilities across the company. You will be involved in the full software lifecycle, from fast-moving prototypes to production-grade systems, enabling CyberArks product lines to adopt agentic workflows safely and at scale. You will collaborate with product managers, researchers, and engineers to translate ideas into robust services, bringing a pragmatic mindset to experimentation, iteration, and operational excellence.
Your Impact:
Engage in the full software lifecycle, from fast-moving prototypes and POCs to production-grade, observable systems.
Collaborate with product managers, researchers, and engineers to translate ideas into robust services.
Work hands-on with leading LLMs and the modern agentic ecosystem, leveraging a wide range of AWS services.
Use cutting-edge frameworks and techniques to develop, evaluate, and continuously improve solutions.
Implement solid design, flexible implementation, strong telemetry, and production feedback loops.

Required AI Engineer & Architect
The Role:
As AI Architect in the ACoE, you are the technical backbone of our internal agentic transformation. You will design and build the reference architectures, standards, and shared infrastructure that enable hundreds of AI agents to run reliably, safely, and at scale - across R&D, Sales, Customer Revenue, HR, Finance, and Marketing.
This is a hands-on, highly visible role. You will split your time between deep technical work (designing agent orchestration patterns, building cross-company automations, evaluating platforms) and enabling others (code reviews, technical mentorship of citizen developers, setting standards). You will report directly to the ACoE Lead and work closely with the CTO, BI, and Platform teams.
Key Responsibilities
Architecture & Standards:
Define and maintain our reference architectures for agent orchestration, tool permissions, memory models, inter-agent communication, and data boundaries
Establish technical standards for agentic development - prompt engineering patterns, evaluation harnesses, testing frameworks, and shared agent templates
Drive tooling standardization across in partnership with the CTO and CFO, and lead the annual vendor/platform review
Build & Enable:
Develop and maintain selected cross-company agentic solutions and automation workflows
Build and maintain the shared infrastructure: monitoring integrations, KPI dashboards, the agent registry, and the ACoE knowledge base
Conduct technical reviews and code reviews for agents before production deployment
Serve as technical SME for citizen developers across all business units - unblocking, guiding, and reviewing their work
Governance Support:
Define and implement the pre-deployment evaluation harness and model card standards
Support the AI Governance Officer (initially the ACoE Lead) with technical input on risk classification, incident response, and rollback planning
Contribute to quarterly ethics audits for high-risk agents
Innovation:
Track the rapidly evolving agentic AI ecosystem and bring relevant insights and tools back
Co-author external technical content (whitepapers, conference presentations) to establish our technical thought leadership.

Your Career:
As a Senior Staff AI Software Engineer on our Generative AI foundation team, you will build AI agents and shared infrastructure that power GenAI capabilities across the company. You will be involved in the full software lifecycle, from fast-moving prototypes to production-grade systems, enabling CyberArks product lines to adopt agentic workflows safely and at scale. You will collaborate with product managers, researchers, and engineers to translate ideas into robust services, bringing a pragmatic mindset to experimentation, iteration, and operational excellence.
Your Impact:
Engage in the full software lifecycle, from fast-moving prototypes and POCs to production-grade, observable systems.
Collaborate with product managers, researchers, and engineers to translate ideas into robust services.
Work hands-on with leading LLMs and the modern agentic ecosystem, leveraging a wide range of AWS services.
Use cutting-edge frameworks and techniques to develop, evaluate, and continuously improve solutions.
Implement solid design, flexible implementation, strong telemetry, and production feedback loops.

We are hiring an AI Researcher to join the research team building the next generation of AI-native security systems. You will work alongside security and threat researchers to build large-scale AI agents that reason over software, code, endpoint activity, and security signals to detect malicious behavior, uncover vulnerabilities, assess risk, and make autonomous security decisions in real-world production environments. We are entering the Mythos era - where attackers operate at machine speed using autonomous systems and AI-generated software, and defenders must evolve the same way. We use state-of-the-art frontier models, including access to Mythos, to build reliable AI-native security systems at global scale. You will help design the evaluations, harnesses, and reliability infrastructure that make autonomous agents dependable under real customer load, while collaborating with leading AI organizations including Anthropic on initiatives such as Glasswing. This is an opportunity to work at the frontier of AI, autonomous systems, and cybersecurity while helping define how the next generation of security systems will operate.
Key Responsibilities
Build AI agents and autonomous security systems that reason over software, code, endpoint activity, MCPs, and security signals to detect malicious behavior, uncover vulnerabilities, and assess risk at production scale.
Develop systems, tooling, and infrastructure that enable agents to autonomously investigate threats, hunt for malware in massive datasets, and operate reliably in complex security environments.
Design and run experiments to evaluate frontier-model and agent capabilities in realistic adversarial scenarios, including benchmark creation, large-scale datasets, automated evaluations, and human-in-the-loop review systems.
Build the evaluation harnesses, observability systems, and reliability infrastructure required to make autonomous agents accurate, scalable, and dependable under real customer load.
Engineer for scale and performance across large distributed AI systems, including inference optimization, orchestration, batching, caching, cost controls, and graceful degradation under high demand.
Continuously evaluate emerging models, agent architectures, prompting techniques, and research directions to ensure our systems remain at the frontier of AI-native cybersecurity.
Rapidly prototype and test new approaches across reasoning, autonomy, evaluations, and security workflows as the AI landscape evolves.
Partner closely with threat and security researchers to extract domain expertise, translate analyst reasoning into AI workflows, and enable new forms of automation and autonomous investigation.
Collaborate with leading AI and security researchers to shape the future of AI-native cybersecurity as the industry transitions into the Mythos era.
Senior candidates will help define research direction, shape technical strategy, identify high-leverage problems, and influence how autonomous AI systems are deployed across the organization.

Within ASIC networking product engineering group, you will help bring AI into product engineering by turning fragmented engineering data into scalable, production-ready solutions for analysis, decision-making, and efficiency.

In this role, you will define and deliver AI solutions that unify data across NVIDIA infrastructure and engineering systems, enabling advanced analytics for production engineering teams through AI agents, copilots, and workflow automation. You will own solutions end to end, from architecture and development through deployment, maintenance, and continuous improvement, and help shape how ASIC networking product engineering uses AI to scale engineering productivity.

What you'll be doing:

Design, build, and maintain AI solutions that improve our division efficiency across production, characterization, analysis, and operational workflows.

Develop agentic analytics capabilities that enable engineers to query, analyze, and reason over ASIC data using AI agents and copilots.

Consolidate data from multiple infrastructure and engineering systems into scalable, reliable pipelines and reusable services.

Partner with production engineering teams to identify pain points, define high-value use cases, and deliver measurable impact.

Build and support tools for data access, automation, reporting, anomaly detection, and engineering insight generation.

Collaborate across us to align interfaces, improve data quality, and support scalable deployment models.

Drive continuous improvement through user feedback, monitoring, and roadmap planning.

Are you a master of the AI lifecycle who can thrive at the intersection of deep learning research, cybersecurity and large-scale production systems? Do you possess the rare ability to jump into a complex technical crisis, diagnose a bottleneck in a Small Language Model (SLM), and lead a team to a production-ready solution?
As a Distinguished AI/ML Architect, you will be the premier technical authority across the Cortex AI/ML organization. You will serve as the "force multiplier" for multiple AI & ML teams, ensuring that our AI strategy - from endpoint-deployed DL/ML models to cloud-scale agentic products - is executed with world-class precision. You will operate as the primary technical architect and hands-on leader for our most ambitious and difficult initiatives.
Key Responsibilities
Operate at the forefront of AI and cybersecurity, leveraging vast datasets to design and deploy innovative defense mechanisms.
Provide horizontal technical leadership across endpoint, cloud, and agentic AI teams to ensure architectural excellence.
Oversee the design and deployment of different model architectures to guarantee scalability and high-performance standards.
Spearhead high-difficulty initiatives and new research frontiers, moving them from ambiguity to production.
Resolve complex technical bottlenecks across the stack, optimizing model efficiency and runtime performance.
Align research and engineering efforts to transform advanced models into robust, production-grade products.

We are looking for one architect to own both layers. Your foundation is deep platform and DevOps architecture: reliability, scalability, security, and cost efficiency of the cloud platform every R&D team builds on. Your growth edge is the AI layer: designing how agents are orchestrated, how they access tools and data safely, how we evaluate whether they work, and how we govern their cost and behavior in production.

We know agentic AI is a young discipline. We are not looking for a decade of AI experience that does not exist. We are looking for a proven platform architect who has spent the last one to two years genuinely building LLM-based and agentic systems in production, and who wants to own where this field goes inside a company that takes it seriously.

What Youll Do:
Platform & DevOps
Cloud Architecture: Own the architecture of our cloud platform, including GKE fleet design, VPC networking, IAM/Workload Identity, and multi-environment strategies across GCP (and AWS where relevant).
IaC & GitOps: Lead Infrastructure-as-Code (Pulumi, Terraform) standards, ArgoCD deployment patterns, and secure CI/CD paved paths (GitHub Actions, GitLab CI) adopted by all R&D teams.
Reliability & Observability: Own reliability architecture, including Disaster Recovery (DR) strategy and drills, P1 incident reduction, MTTR improvement, and observability platform architecture (Datadog, Prometheus, Grafana, OpenTelemetry) including usage and cost optimization.
FinOps: Drive FinOps as an architectural discipline-rightsizing, idle-resource elimination, unallocated-spend attribution, and cost-aware design reviews using tools like Kubecost or GCP Cost Management.
Agentic AI

Agent Architecture: Design our agentic AI ecosystem, focusing on agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen), tool/MCP (Model Context Protocol) interfaces, vector databases/memory strategies (e.g., Pinecone, Qdrant, pgvector), and production AI deployment patterns.
Evaluation & Guardrails: Build the evaluation and guardrail layer for AI in production using frameworks like Ragas, TruLens, or LangSmith. Define permission models, human-in-the-loop checkpoints, audit logging, and policy-as-code for AI usage.
AI FinOps & Monitoring: Own AI cost observability, token spend monitoring, model routing strategies (optimizing for cost/quality/latency trade-offs), and real-time alerting on runaway model usage.
Reference Architectures & Standards: Define reference architectures for teams building AI features, RAG patterns, prompt versioning/management, and model API standards (OpenAI API, Anthropic, open-source models via vLLM/Ollama), driving high-leverage AI automation across R&D.

Were looking for a Generative AI Developer to join our forward-thinking engineering team. This role is perfect for someone with a passion for cutting-edge AI, a strong software engineering background, and the creative spark to identify and implement novel use cases within our product.
You will play a critical role in adding AI capabilities to our FinOps SaaS platform. Whether it's enhancing user workflows, automating insights, or inventing entirely new product experiences, youll have both the freedom and support to experiment and execute.
We provide a uniquely rich dataset covering the full scope of a companys cloud spend. This expansive data playground offers a powerful foundation for experimentation and insight generation, enabling the development of intelligent, value-driven features.
Responsibilities:
Lead the charge in transforming our product and preparing it for the agentic age.
Design, build, and deploy generative AI-powered features across our product.
Identify opportunities for AI integration by proactively exploring FinOps use cases and user needs
Prototype and validate new AI use cases quickly and iterate based on internal and external feedback
Collaborate cross-functionally with product, design, and backend teams to drive innovation from concept to production
Stay current with the fast-moving generative AI landscape and evaluate new models, APIs, and tools (e.g., OpenAI, Anthropic, Hugging Face, AWS Bedrock, open-source LLMs).
Live in the future and track new innovations and paradigms in this fast evolving field and identify opportunities to integrate them into the product
Implement safeguards, prompt engineering techniques, and usage monitoring to ensure high-quality AI outputs
Optimize model performance, inference time, and cost efficiency within AWS infrastructure.

we are building an agentic development lifecycle, an infrastructure of autonomous agents that work alongside our engineers to accelerate and improve how we build software. Our goal is to ship faster, with higher quality, and to continuously tighten the feedback loop between what the agents produce and what engineering actually needs. Over time, this system should compound: every improvement makes the next one easier to reach.
we are an enterprise secure browser used by some of the largest organizations in the world. It's a complex, multidisciplinary product spanning browser core, frontend, extensions, and backend services, and it runs at scale for customers who need it to always work. The bar for what we ship is high. That means whatever agentic infrastructure we build has to meet the same standard. We're not here to vibe code our way to production.
We're looking for an AI Engineer with a product builder's mindset. You have real experience with AI and agentic workflows, and you know how to take a complex project from idea to adoption, technically and organizationally. That means working across teams, aligning with security, infrastructure, and other engineering groups, and understanding that building the system is only half the job. Getting people to trust it is the other half.
We aren't looking for a conventional senior developer; we need someone whose mindset is adapted to technical challenges that didn't even exist 18 months ago.

Artlist is a leading creative technology company on a mission to empower creators and brands to bring their vision to life with video. Offering cutting-edge AI tools and models for image, video, and voiceover creation, alongside high-quality creative assets and powerful editing tools, Artlist enables creators to stay on trend, and achieve their creative goals. Trusted by over 30 million creators worldwide and top brands including Google, Amazon, Microsoft, and Versace, Artlist provides a seamless, subscription-based platform with a global license, giving creators everything they need to produce professional video content efficiently. For more information, visit artlist.io We're looking for a talented Customer Experience AI Chatbot Coordinator to join our CX team. In this role, you will sit at the intersection of CX strategy and AI workflow design- using natural language prompting, intent architecture, and conversation flow expertise to drive ticket deflection and improve the customer journey. Someone with a strong instinct for how customers communicate, and the ability to translate that understanding into AI workflows that resolve issues without human intervention. Key Responsibilities
* Design, build, and iterate on conversational flows and NLP prompts within third-party AI chatbot platforms
* Develop intent libraries, training phrases, and response templates that accurately reflect customer needs and Artlist's brand voice
* Own the ongoing training and tuning of the chatbot to improve intent recognition and resolution rates
* Continuously refine bot behaviour based on performance data, conversation logs, and agent feedback
* Monitor key metrics - especially ticket deflection, containment, and CSAT - and implement targeted optimisations
* Contribute to the chatbot roadmap with ideas for new flows, use cases, and automation opportunities
* Work closely with the CX and Experience team to understand recurring issues, escalation patterns, and agent pain points
* Maintain clear, up-to-date documentation of all flows, training data, intent structures, and change logs

We are looking for a Distinguished Engineer to help shape the future of AI infrastructure. As part of our advanced development and research efforts, you will drive technical vision and innovation across the AI software stack, spanning GPU computing, RDMA, NVLink, storage, and large-scale distributed systems. You will research next-generation system designs, influence product and platform direction, and help define how massive-scale AI environments are built, optimized, and operated through hardware-software co-design.

What youll be doing:

Research, define, and drive co-design solutions across AI software stacks involving GPU computing, RDMA networking, NVLink interconnects, storage, and large-scale distributed infrastructure.

Lead deep technical investigations and proof-of-concept development in areas related to distributed AI, deep learning systems, high-performance computing, virtualization, memory management, and large-scale systems design.

Work closely with multiple teams across us to advance next-generation AI infrastructure technologies and influence cross-stack co-design decisions.

Analyze end-to-end system bottlenecks and opportunities across compute, networking, memory, and storage layers to improve performance, scalability, and efficiency.

Guide the design of environments that support massive-scale AI training and inference workloads in production.

We're looking for a Senior Software Engineer, Infrastructure & Automation to join and play a key role in building the engineering infrastructure behind our AI-powered offensive security platform.

You'll design and build the infrastructure required to automatically validate and improve our platform and agents, including AI-powered agents for testing, evaluating, and automation of our systems that ensure our platform infrastructure and AI agents perform is reliable, repeatable, and effective at scale. Working closely with our AI Research, Security Research, and Engineering teams, among other things, you'll develop the infrastructure that enables rapid experimentation, deterministic validation, and continuous evaluation of autonomous AI attackers.

If you're excited about building developer platforms, testing infrastructure, and complex distributed systems in a fast-moving AI startup, we'd love to meet you.

Responsibilites
Design and build the testing and automation infrastructure that powers Tenzais platform and autonomous security agents. Define and shape the platform architecture for that goal.
Build deterministic and indeterministic validation tools for platform behavior and repeatable, data-driven evaluation for non-deterministic agent behavior.
Help develop continuous evaluation pipelines that measure performance, compare models and releases, detect regressions, and surface actionable results.
Build Python services and automation that orchestrate distributed test runs and manage their environments, inputs, and results.
Create internal tools and agents and CI/CD integrations that improve experimentation speed, developer productivity, and release confidence.
Partner with AI Research and Security Research to define evaluation methodologies, success criteria, and coverage.
Own systems end to end - from architecture and implementation through production reliability and continued evolution.
Contribute to technical design, architecture discussions, and code reviews across the engineering organization.