לוח משרות דרושים AI Engineer בתל אביב יפו, לחיפוש זה נמצאו 98 משרות

We are looking for an AI Engineer to build the backbone of our autonomous security platform.
our company builds AI Digital Employees who help cyber teams close the execution gap. Our first AI Digital Employee, Alex, learns, understands and takes away the burden of Identity and Access Management (IAM) tasks - proactively completing the organization's cyber objectives.
About the Role:
Youll design, build, and ship the AI systems behind Alex: production agents that reason over customer identity data, call real tools, and complete IAM tasks end to end. Starting from a cybersecurity need, youll frame the technical problem, choose an approach, and own the solution through production: architecture, implementation, evaluation, deployment, and iteration on real customer behavior.
Youll build on our companys shared agent infrastructure and extend it: new tools and integrations, workflow orchestration, context and memory management, evaluation harnesses, and the guardrails that keep an autonomous agent safe inside a customers environment.
This is a hands-on engineering role, not a research position. The goal is not to prove that an idea can work; it is to make it useful, reliable, and observable for customers in production.
What You'll Do:
Design, implement, and ship AI-powered features for complex cybersecurity workflows, from prototype to production.
Build the systems that make agents work in practice: tool integrations, planning and orchestration logic, context management, structured outputs, and recovery from failures.
Choose the right technical approach for each problem, combining models, deterministic logic, data, and conventional software where appropriate.
Define quality for each use case and build the evaluation pipelines that measure correctness, task completion, consistency, safety, latency, and cost, and wire them into CI and release.
Debug agent behavior in production: trace failures to their root cause in the prompt, tool, data, or architecture, and fix it.
Own the reliability, observability, and cost of the agent systems you build.
Collaborate with cybersecurity experts, product, and platform engineers throughout development.

You will be part of the Platform team, reporting to the VP of R&D. This is a hands-on IC role at the crossroads of data science, software engineering and cloud infrastructure: you design the service, you build every layer of it, you ship it, you measure what it does for the products that use it, and you maintain and extend it with the features its users need.
Responsibilities
Own services end to end: From a one-page design through API, SDK, UI and infrastructure to production, built to the platforms service standards: tested at every level, auto-deployed on merge, with live status and cost per job.
Build data services at scale: Spark and EMR jobs over the lakehouse, orchestrated in Airflow, that resolve identities, join vendor feeds and score millions of records per run, and that get faster and cheaper with every release.
Put models and LLMs into production: Foundation model, embedding, classification and agent services, each with an evaluation harness behind it, so a model change is measured before a product feels it.
Build the full stack: Backend services, web consoles and SDKs that an engineer, a product team or an agent can use self-serve, with a user guide for people and a playbook for agents.
Run what you ship: Monitoring, alerting, retries and cost per job. You are the first responder when your service is red, and you announce every release to its users with measured numbers.
Work AI-native: AI coding agents are part of how we build every day. You drive them, review their output critically, and build services that agents consume as easily as people do.

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As an AI Algorithm Engineer , youll design and implement advanced algorithmic workflows that power our data digestion pipelines and conversational AI agent experience. You will:
Architect and build workflows that turn raw, unstructured data (e.g., PDF documents and ultra high-resolution architectural drawings) into meaningful, structured context for downstream analysis.
Combine techniques from classical NLP, computer vision, unsupervised learning, and graph theory to build robust end-to-end pipelines - not just standard LLM API calls, but intelligent decomposition, structuring, and context engineering.
Develop AI agent workflows that let customers explore, query, and reason about their data naturally and reliably.
Build strong evaluation infrastructure to benchmark and continuously improve both classical algorithmic components and LLM-based workflows.
Work primarily in Python, and collaborate across systems and services in TypeScript when needed.
Move fast from prototype to production, while maintaining correctness, scalability, and measurable quality.

Were looking for a hands-on AI Solutions Manager who combines deep practical knowledge of generative AI tools with strong business understanding. This role sits at the intersection of technology and impact - identifying real organizational needs and translating them into scalable, AI-driven solutions.

This role is perfect for someone who lives and breathes AI technology, stays ahead of the rapidly evolving landscape, and knows how to turn cutting-edge capabilities into real-world solutions.

What Youll Do

Collaborate with cross-functional teams to identify opportunities where AI can solve complex business and internal problems
Evaluate and select the right AI tools and models (Claude, Gemini, OpenAI, etc.) based on use case, limitations, cost, performance, and scalability
Design and implement AI-powered solutions and internal automations using LLM APIs and workflow platforms (e.g., N8N, Workato)
Build and optimize prompts, RAG pipelines, and AI agents while understanding model limitations and trade-offs
Architect scalable AI workflows and integrations across systems (e.g., Salesforce, Jira, Confluence, , etc.)
Lead hands-on projects from ideation to rollout, iterating rapidly to deliver value
Stay up to date with emerging AI capabilities and continuously evaluate new tools and frameworks
Educate and guide the organization how to adopt and use wisely AI tools and models

We are looking for a Senior Data Science Researcher to join Honeycomb's AI team, the team behind underwriting decisions, quote-time risk scoring, and portfolio analytics.

In this role, you will own entire research directions: framing the problem, deciding what's worth measuring, designing the approach from first principles, and turning
the result into production signal. The work is production-oriented applied research on hard, real-world datasets where standard recipes don't apply and the right method
often has to be invented for the problem.

What You'll Do

- Own open-ended research directions end-to-end - from a vague business question to a deployed, validated signal.
- Work across the team's core research areas - risk modeling, environmental and catastrophe modeling, and behavioral modeling - translating complex real-world processes
into reliable predictive signal.
- Deeply investigate existing production models - understand what they actually learn, where they break, and where the headroom is; identify and ship optimizations
grounded in that understanding.
- Design approaches from first principles when off-the-shelf methods don't fit the data.
- Build rigorous evaluations and own the result in production, not just the notebook.
- Document research clearly so findings are reproducible, auditable, and compound over time.

We are seeking a AI Engineer to join our team and implement advanced AI solutions in our travel and expense management products. The ideal candidate will have strong backend engineering expertise and enthusiasm for working with cutting-edge AI technologies.
You'll be responsible for designing and implementing backend services that integrate AI capabilities including large language models (LLMs), retrieval-augmented generation (RAG), and conversational interfaces into our consumer-facing products. This is an opportunity to expand your expertise into AI while leveraging your strong engineering foundation.
What Youll Do:
Design and develop scalable, high-performance backend services and APIs.
Implement integrations with AI platforms and services.
Work with AI specialists to translate AI capabilities into production-ready features.
Ensure high reliability, performance, and security of AI-powered systems.
Collaborate with product and engineering teams to gather requirements and deliver solutions.

Were looking for a talented AI-native Software Engineer to join our innovative R&D team!
We help our customers acquire better users and spend less doing it - and our core team owns the engine behind it. We turn raw customer data into ad-network conversions, end-to-end: through large-scale ML and data pipelines, event-driven delivery services on AWS, and the UI that ties it together. There's no narrow lane here - you own problems wherever they live in the stack.
We're an AI-native team: we treat AI and agentic tooling as a first-class part of how we build, and it's how a small team credibly owns this much surface area. We're looking for a high-ownership generalist who works this way (or is hungry to), and who thrives in a correctness-critical domain where a bug means real customer ad-spend going the wrong way.
Responsibilities:
Turn raw customer data into ad-network conversions end to end, building across the whole stack: ML and data pipelines, delivery services, and the customer-facing UI.
Take features from idea to production largely on your own, designing, shipping, monitoring, and iterating, using AI and agentic tooling as a force multiplier, and owning your systems in production where mistakes translate directly into customer spend.
Collaborate closely with the team lead, product, and data scientists to take models and ideas from prototype to reliable, scaled-up production.

we are looking for a Senior Backend Engineer with a strong passion for AI and intelligent systems to join our growing R&D team.
In this role, youll be building the backend foundation that powers our AI-enhanced security analytics, autonomous detection, and real-time decisioning systems. Youll work closely with AI engineers, product, and security experts to bring agentic intelligence into our companys runtime platform - transforming how modern apps defend themselves.
Youll architect and implement services that scale to massive volumes of runtime data, integrate large language models (LLMs), and support RAG-based workflows for contextual understanding and response.
Youll Be Great For This Role If You Love To:
Design and build scalable, resilient backend services that power our companys AI-driven runtime security platform.
Integrate LLMs and agentic AI components into production environments to enable autonomous detection and remediation capabilities.
Design evaluation frameworks for measuring model performance and reliability in real-world conditions.
Develop Retrieval-Augmented Generation (RAG) pipelines tailored to application security data.
Own backend architecture decisions - from data modeling to performance optimization - across Node.js, Python, and cloud-native environments.
Contribute to our infrastructure, leveraging CI/CD, Kubernetes, Docker, and modern observability practices.
Mentor peers and help shape our companys AI engineering culture.

our company already runs agents in production. Our WAF Copilot takes a freshly disclosed CVE and turns it into a validated, provider-specific WAF rule - a state-driven agent graph that researches the vulnerability, maps the attack surface, composes the rule, then attacks its own output with an independent bypass judge and a false-positive prober before a human is ever offered a deploy. When a weakness survives, the system downgrades its own recommendation and records what it could not prove.
That last part is the thesis of this role.
An agent that touches production security must prove it works and declare what it couldn't prove. The security industry is about to be flooded with agentic claims nobody can verify. We intend to be the company that made verification legible - and that starts with holding our own agents to a standard we're willing to publish.
We're looking for an AI Tech Lead to own that standard across three surfaces:
The platform - Today each agent flow is close to a bespoke implementation. You'll turn our hard-won patterns into shared components, conventions, and infrastructure so the next agent is a week of work rather than a quarter - with evaluation, observability, and cost control built in rather than bolted on.
Enablement - our company's advantage compounds only if the whole company is AI-fluent, not just R&D. You'll raise that fluency everywhere - engineering, research, product, GTM - through tooling, patterns, and teaching.
The voice - You'll publish the methodology: how we benchmark agentic security output, how we model residual risk, what we learned failing. This is a category-defining position and we want it argued in public.
This is a hands-on lead role with no direct reports. Your authority comes from the quality of what you build and how clearly you explain it.
What You'll Do
Define and build our company's agent framework - shared components, orchestration patterns, tool interfaces, and conventions that make "how do I build an agent here" a five-minute answer.
Make evaluation a gate, not an afterthought: trajectory testing, golden datasets, offline replay, and per-dimension scoring that runs in CI. Land the release benchmark so promote / hold / rollback is a number, not a vibe.
Own agent observability end to end - the execution graph, tool invocations, intermediate reasoning, latency per step, and quality drift - including the external Temporal-orchestrated flows that are hardest to introspect today.
Own the economics: provider abstraction, model routing by task complexity, small-model substitution where it holds, cost attribution per flow. Thousands of CVEs through a labeling agent is a budget line, not a detail.
Drive latency and determinism in our production agent flows - tighter loops, early stopping, deterministic state machines over prose-in-prompt orchestration.
Raise the company's AI fluency. Build the internal tooling, skills, and playbooks that let every team - not just R&D - work AI-natively, and teach the judgment for when not to.
Partner with security research, product, and engineering leadership so agent capability and product roadmap actually converge.

The Applied AI Strategist helps customers turn complex business needs into practical AI solutions. This role combines business understanding, applied AI fluency, product thinking, and strong execution. You will work closely with customers and internal teams to understand problems, define solutions, guide implementation, and ensure the resulting systems deliver measurable value. You will bring structure to ambiguous challenges, make thoughtful trade-offs, and help move AI initiatives from concept to reliable production use.

What You'll do
Develop a deep understanding of customer workflows, requirements, constraints, and goals.
Identify and prioritize high-value opportunities for applying AI.
Translate ambiguous business needs into clear solution plans, requirements, and success criteria.
Design and validate solution concepts through prototypes, demonstrations, and structured evaluations.
Define prompt, context, workflow, and evaluation approaches for AI-powered solutions.
Coordinate stakeholders and maintain alignment throughout implementation.
Guide trade-offs across speed, quality, reliability, scalability, governance, and customer impact.
Identify and resolve risks or blockers that could affect successful delivery.
Measure solution performance, adoption, and business value.
Turn recurring customer needs and implementation lessons into reusable capabilities and best practices.

We're looking for an experienced AI Engineer to join our core team and design, build, and implement the autonomous AI agents that power our company's revolutionary approach to eliminating container vulnerabilities at the source.
Responsibilities:
Explore and prototype cutting-edge AI techniques to tackle our hardest challenges
Architect and develop autonomous AI agents that intelligently eliminate vulnerabilities during container image generation
Collaborate with product and engineering teams to bring AI-driven features from concept to production
Monitor and evaluate AI model performance, implementing feedback loops to continuously improve accuracy and reliability
Develop and maintain machine learning pipelines that process security data and generate actionable insights
Ensure AI systems are robust, reliable, and performant at enterprise scale.

Were hiring an AI Engineer to design and build the intelligence layer behind our autonomous offensive security platform.

Youll develop AI agents capable of performing vulnerability discovery and exploitation at the level of top human white-hat hackers - combining deep security expertise with advanced AI reasoning and decision-making.

This role, reporting directly to our Agent TL, offers the opportunity to define how AI transforms offensive cybersecurity.

Youll collaborate closely with AI researchers, software engineers, and security experts to translate penetration-testing workflows into intelligent, automated capabilities - systems that plan, execute, and adapt in real time.

If youre passionate about AI, thrive in fast-paced environments, and want to shape the future of autonomous cybersecurity - wed love to meet you.

Responsibilities
Design and implement autonomous AI agents for penetration testing and vulnerability exploitation.
Translate offensive security workflows into intelligent, multi-step reasoning systems.
Develop modules for exploit generation, payload delivery, and adaptive vulnerability scanning.
Collaborate with AI researchers to fine-tune and evaluate models that enhance reasoning, adaptability, and precision.
Build evaluation frameworks to measure exploit success, learning performance, and reliability.
Ship production-ready AI systems that improve the speed, depth, and scalability of security testing.
Establish best practices for safe and controlled deployment of AI agents in offensive environments.

We're looking for an AI Researcher to help design and enhance core intelligence capabilities. You'll work at the frontier of large language models and conversational AI - run experiments, develop novel techniques, and transform research breakthroughs directly into product impact. This is a role for someone who wants to research real-world challenges and ship to production: you'll be pushing the state of the art in domains like conversational dynamics, speech understanding, and agentic question answering, while seeing your work reach millions of language learners.
What You'll Do:
Drive original AI research across LLMs, dialogue systems, speech, and language understanding - from hypothesis through design and all the way to deployment
Design and run rigorous experiments; develop novel model architectures, fine-tuning strategies, and training methodologies
Own the full research lifecycle - ideation, experimentation, evaluation, and deployment into production
Apply and extend state-of-the-art techniques (RL, instruction tuning, RAG, agentic workflows, multimodal models) to real user-facing challenges
Collaborate with engineering and product to transform research findings into shipped features
Define research direction in your area and contribute to long-term AI roadmap

We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies.

You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you.

Responsibilities:
Build, train, and ship detection models end-to-end, from raw data to production.
Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results.
Partner with security researchers to turn security research outputs and domain expertise into detection capabilities.
Own evaluation: design benchmarks, build labeled datasets, and define production standards.
Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability.
Iterate fast, with a tight feedback loop between model performance and product outcomes.

This is a role for someone who wants to help build the org of the future, where agents and people work side by side. A strong software engineer who wants to own complete solutions rather than one piece of a larger system. Youll be expected to figure out how to solve the problem and make it work.

What Youll Do
Take a problem from fuzzy to shipped - work from a rough definition and a priority, and figure out the rest.
Architect, build, and ship AI agents, applications, and intelligent workflows end-to-end. Integrate AI solutions with company data, APIs, and internal systems.
Rapidly prototype and experiment with new AI technologies, turning successful concepts into reliable production solutions.
Embed with internal users - sit with the team, watch how they work, map the process, build what fixes the highest-leverage part.
Own the build end-to-end - architecture, implementation, evals, logging, deployment.
Debug the non-deterministic - when a system fails on edge cases, you build the evals and guardrails to catch it rather than reading logs by hand.
Stay honest about what AI can and cant do - scope realistically, ship reliably, dont overpromise.