לוח משרות דרושים הייטק AI  בתל אביב יפו, לחיפוש זה נמצאו 174 משרות

What You Will Do:
Research and prototype novel approaches to application security that go beyond traditional AppSec tooling.
Build proofs-of-concept for advanced prevention, detection, triage, and remediation capabilities.
Design experiments and validate new security features through data-driven testing.
Partner with product and engineering to bring successful POCs into the core platform.
Evaluate and optimize LLM-based agents and model outputs for security use cases.

The Cortex Content team powers Palo Alto Networks 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.

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.

The company 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.

We are looking for a senior product manager to join our AI Unit and help shape the AI platform that powers next generation of products.

This role focuses on the platform and ecosystem layer of AI, building the capabilities, standards, and systems that enable teams across the company to create AI-powered experiences effectively.

You will work closely with engineering, data, and product teams to define how AI capabilities are built, reused, and governed across the platform.

This is a high-impact role with broad scope. It requires strong product fundamentals, technical curiosity, and the ability to operate in an evolving space.

We develop the AI infrastructure that powers the AI experience throughout the product. We ensure the production AI platform is stable, reliable and secure.
Our mission is not only to build AI-powered features, but to enable every product team to build reliable, scalable, and high-quality AI experiences.
We work with modern LLM technologies, agentic workflows, MCP, retrieval systems, evaluation pipelines, distributed systems, and production-grade AI infrastructure, deploying to production multiple times a day.
Job responsibilities
What You'll Do
As a Tech Lead, you'll play a key role in shaping the future of AI .
You'll combine deep technical expertise with strategic thinking, helping multiple teams build production-ready AI experiences while driving engineering excellence across the organization.
In this role you will:
Lead the technical direction of one of AI domains.
Design scalable AI architectures and production-ready agentic systems.
Drive technical decisions around LLMs, Retrieval, Tool Calling, MCP, evaluation frameworks, and AI infrastructure.
Own complex cross-team initiatives from design through production.
Collaborate closely with Product, Data, Infrastructure, and Engineering teams to turn AI opportunities into customer value.
Help define engineering standards and best practices for building reliable AI systems.
Drive engineering excellence through architecture reviews, mentoring, and technical leadership.
Balance rapid experimentation with production-grade quality and scalability.
Evaluate emerging AI technologies and identify opportunities to improve our platform and customer experience.

The Data Group builds and maintains the core data assets that power our products, from our Company and Contact databases to our Connect and Recommendation systems. The Data Tech Lead sits at the forefront of that mission, leading engineering innovation that directly shapes the experience of our growing user base and drives the evolution of intelligent data services.

This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office.

Your impact and responsibilities:

Leading the design and development of scalable, high-performance data workflows, including both batch pipelines and real-time data products.
Defining, implementing, and enforcing engineering best practices related to code quality, testing, CI/CD pipelines, observability, and documentation.
Mentoring, supporting, and growing a team of data engineers, fostering a collaborative and high-performance engineering culture.
Identifying opportunities to create new data assets and features that expand product capabilities and value proposition.
Driving architectural decision-making in areas of data modeling, storage solutions, and compute resources within cloud environments such as Databricks and Snowflake.
Collaborating closely with cross-functional stakeholders- including Product, DevOps, and R&D - to ensure effective delivery and platform stability.
Promoting and championing a data-driven mindset across the organization, balancing technical rigor with business context and strategic goals.

As an AI Product Owner & AI Process and Agentic Designer, you will own the process of transforming complex workflows into scalable AI solutions, bridging the gap between business stakeholders and AI Engineers. You will lead business process analysis, identify agentic process opportunities, validate business value, and define solution designs that enable successful AI implementation across the organization.

Responsibilities

Business Process Analysis & Discovery
Partner with business stakeholders to map, document, and analyze existing workflows, identifying inefficiencies, bottlenecks, and manual processes ripe for automation.
Conduct discovery sessions and workshops to deeply understand business needs, pain points, and success criteria across departments.
Agentic Opportunity Identification
Evaluate workflows to pinpoint where agentic and AI-driven approaches can deliver the most value, distinguishing tasks suited to automation, augmentation, or full agentic orchestration.
Maintain a prioritized pipeline of AI/agentic opportunities, assessing each for feasibility, complexity, and impact.
Business Value Validation
Define and quantify the expected business value of proposed solutions (e.g., cost savings, time reduction, quality improvement, revenue impact) and build the business case for investment.
Establish success metrics and KPIs, and track realized value post-implementation to ensure outcomes match projections.
Solution Design & Definition
Translate business requirements into clear solution designs, including agentic process flows, decision logic, data inputs/outputs, integration points, and human-in-the-loop checkpoints.
Define guardrails, escalation paths, and fallback behaviors to ensure solutions operate safely and reliably.
Stakeholder & Engineering Bridging
Serve as the primary liaison between business stakeholders and AI engineers, translating business intent into technical requirements and engineering constraints back into business terms.
Own the product backlog, prioritize features, and clearly communicate scope, trade-offs, and timelines to all parties.
Implementation Enablement & Iteration
Guide solutions through the full lifecycle from concept to deployment, supporting testing, validation, and change management.
Gather feedback, monitor performance, and drive continuous refinement of deployed AI and agentic solutions.
Scalability & Standardization
Identify reusable patterns, components, and best practices to scale successful solutions across the organization.
Contribute to governance frameworks, documentation standards, and design principles for AI/agentic product development.

we are seeking an experienced and visionary ML Engineer to join our dynamic technology organization. The successful candidate will be a part of a team of talented AI Engineers, Building agentic AI solutions, driving innovation and delivering business value through advanced Generative AI solutions & machine learning techniques. This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge Gen AI and LLM methodologies and a passion for continuous learning and development.

Responsibilities

Build the solution: Own end-to-end technical delivery of agentic systems, from source-system integration through agent design, development, evaluation, and production deployment.
Integrate AI systems with source systems (Salesforce, Databricks, Splunk, internal APIs, business applications). Handle agent harness and orchestration.
Handle the operational handover to the business function and any necessary support transition.
Collaborate across teams: Partner closely with data engineering, platform, security, and business teams to align on requirements, dependencies, and integration points.
Engage stakeholders: Gather requirements directly from business functions, communicate technical trade-offs clearly, and keep stakeholders informed on progress, risks, and timelines.
Uphold quality and reliability: Establish and maintain best practices for code quality, testing, evaluation, monitoring, and observability of deployed AI systems.
Contribute to the team's technical growth through knowledge-sharing and help shape engineering standards and reusable patterns.
Stay current: Continuously evaluate emerging Gen AI, LLM, and agentic frameworks, and recommend tools and approaches that improve delivery speed and solution quality.

We are seeking a Research Scientist to join its Fundamental AI Research (FAIR) organization, focused on making significant advances in Code Generation and LLMs reasoning with specific focus on reinforcement learning, synthetic data generation and advanced scaffolding/agentic techniques. You will have the opportunity to work with a broad and highly interdisciplinary team of scientists, engineers, and cross-functional partners, and will have access to cutting edge technology, resources, and research facilities.
AI Research Scientist Responsibilities
Lead, collaborate, and execute on research that pushes forward the state of the art in agentic code generation
Work towards long-term ambitious research goals, while identifying intermediate milestones
Directly contribute to experiments, including designing experimental details, implement reusable code, running evaluations, and organizing results
Contribute to publications and open-sourcing efforts
Mentor other team members. Play a significant role in healthy cross-functional collaboration.

Our Fundamental AI Research (FAIR) organization is seeking a Research Engineer to drive advancements in generative models. The role involves working across the full spectrum of research, engineering, and optimization for frontier model efforts.
Research Engineer, Fundamental AI Research (FAIR) - Generative Models/LLM Acceleration Responsibilities
Innovate, lead, and execute pioneering algorithmic research to push the state-of-the-art in generative models and LLM performance
Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results
Collaborate with cross-functional teams (research, product, infra) to build new and advance LLM optimization
Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
Lead initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
Take a significant role in components, features, or systems with good end-to-end understanding
Contribute to publications, open-sourcing initiatives, and mentor other team members.

We are looking for a VP Data Intelligence & AI Enablement to join our Team.

Why is this role so important?

As VP of Data Intelligence & AI Enablement, you will own the data foundation that powers AI-native future.
You'll lead the BI team and the Data Analyst teams embedded in GTM (Sales, Marketing) and Product- teams that are already AI-fluent, working daily with Cursor, Git, and LLM tooling.
Your mandate is to channel that capability into building production-grade agentic solutions that drive measurable business outcomes across GTM, Product, and G&A.



So, what will you be doing all day?

AI Infrastructure (MCP): Develop the internal data and API layer to make business data accessible, safe, and actionable for AI agents across GTM and Product.

Proactive Intelligence: Shift the company from traditional reporting to automated, AI-pushed insights and dashboards delivered directly to stakeholders.

Semantic Layer & Governance: Build and own the unified semantic layer to ensure a single source of truth and eliminate metric misalignment across Sales, Marketing, CS, and Product.

Scaling Analytics with AI: Lead and upscale the data analysts function into "AI solution builders," implementing automated agentic workflows (using Cursor, Git, LLMs) to handle complex processes.

GTM & Product Analytics: Own the core analytical frameworks for key business metrics (Pipeline, Conversion, CARR, Retention, and Product Usage).

C-Suite Partnership: Serve as the strategic data thought-partner to the executive team (COO, CRO, CPO, CMO, CFO) to align data strategy with business priorities.

We are looking for a Principal Tech Lead to join our team.

So, what will you be doing all day?

As a Principal Tech Lead, your daily impact spans deep-tech execution, organizational mentorship, and bleeding-edge paradigm adoption:

Architectural Governance: Own the end-to-end design, scalability, and performance of complex backend distributed systems. Ensure architectures are resilient, modular, and optimized for intensive data workloads.
Agentic Engineering Innovation: Champion and embed Agentic SDLC paradigms, tools and solutions into the engineering lifecycle. Design frameworks that enable autonomous AI agents to build, test, and deploy features end-to-end, while enforcing rigorous quality gates and deterministic production guardrails.
Cross-Project Orchestration: Maintain an exceptionally large context window to actively guide and facilitate multiple highly technical streams and parallel projects simultaneously without losing focus or delivery speed.
Guild Leadership & Mentorship: Act as the primary technical sounding board for the engineering community. Lead backend guild sessions, introduce emerging technologies, and actively mentor senior and staff engineers to elevate the group's collective capabilities.
High-Velocity Execution: Lead by example. When taking on critical path, hands-on tasks, execute with unmatched speed and quality, demonstrating what peak operational efficiency looks like.

As an AI Engineer , you will be a key player in building our new AI platform, AI Studio. This platform aims to provide users with instant answers to complex market research questions, generate world-class reports, and create live, interactive, and powerful dashboards. Our goal is to reduce the complexity and time-to-insight for our users, helping them streamline their market tasks and achieve automation that drives real value.

So, what will you be doing all day?

Design and develop LLM-based intelligent agents that can handle complex market research queries.
Utilize LangGraph and other frameworks to create state of the art AI agents that integrate various tools and data sources.
Implement integrations that allow agents to take automated actions on behalf of users, streamlining workflows and reducing overhead.
Collaborate with product, data, and UX teams to ensure that AI solutions are aligned with user needs and business goals.
Continuously evaluate and improve the performance of AI models to ensure high-quality and data-driven results.

Required Product Manager - AI Platform
About the role:
We've taken a platform-first, builder mindset from day one, developing the modular building blocks we call pillars. Our users are exceptional engineers who continuously build creative and unexpected solutions with our platform. Being a PM here means embracing that creative tension while staying relentlessly focused on where the platform needs to go next.
As a PM, you'll own a pillar end-to-end alongside your team of engineers and designers. You'll build and evolve it as a standalone value stream and as a connected part of the broader platform. PMs are full picture owners: they shape the story of their pillar from market positioning to technical enablement, defining success not just by what gets shipped, but by the measurable impact it delivers.
What you'll do:
Define what to build and why. Go deep with customers, analyze usage data, and research the competitive landscape. Bring that view to your team, build a sharp perspective on what's worth building next, and communicate the reasoning to engineering, leadership, and the market.
Shape experiences engineers choose to use. Our developer experience spans the entire ecosystem across the UI, API, CLI, and daily chat and coding interfaces. Work closely with design and engineering to make each surface deliberate, from the first wireframe through launch.
Drive cross-functional impact. PMs don't hand off and wait. You work across engineering, design, marketing, sales, and CS throughout the full product cycle, accountable for picking the right problem, the solution built, and whether the launch lands.
Build for agents and developers. We are the context and action layer for both. Shape your domain so agents can act on it reliably and developers can navigate it intuitively. Understand the tradeoffs in how data, APIs, and interfaces serve both. Separate the signal from the hype, and use that view to sharpen your roadmap.
Measure what matters. Define success metrics before you ship. Track adoption, analyze what's landing, and iterate until the outcome is real.