our company's product is built around an AI agent that security analysts and detection engineers work with directly. It investigates coverage questions against live enterprise security data, authors and tests detection logic, and tunes noisy alerting.
That agent is already in production with enterprise design partners. Now we need to make it dependable and scalable enough for GA.
You will own that evolution: the agent architecture, its evaluation and quality system, and the production engineering around it. This is a hands-on senior IC role with real architectural authority - you set the technical direction and you write the code.
The agent operates inside customer security environments, where a wrong action can become a customer incident. Correctness, isolation, observability, and evaluation are not polish. They are the product.
What you'll be doing
Agent architecture: Design the evolution from today's production single-agent system to a multi-agent one: orchestration, task decomposition, runtime and framework choices, and a migration path that does not break what design partners already rely on.
Agent capability: Own the prompts, context, skills, and tool design that make the agent genuinely good at detection engineering across multiple security platforms, not just plausible-sounding.
Evaluation platform: Build the harnesses, judges, and golden datasets that turn "the agent feels better" into a number, plus the CI gates that keep regressions from shipping.
Reliability and safety: Keep long-running agentic sessions healthy in production, and build the isolation and guardrails required of an agent working inside enterprise security environments.
Production debugging: Work real failures from production traces, and turn each one into an eval case that can never regress silently.
Technical direction: Make the calls on architecture, sequencing, and quality bar and be accountable for the outcome, including raising how AI-natively the whole team builds.
Cross-team partnership: Partner with product and customer-facing teams on what the agent should do, and with platform teams on the data and integrations it depends on.

A security company, is building an internal AI capability across the organization: agents that perform real tasks as the organization, with ROI and security assessed at every step. We are looking for a senior engineer who owns this end to end - a hands-on builder who also carries the senior function: setting the technical strategy, making the architecture calls, and being the person leadership trusts on internal AI.
This is not a pure IC build role and not a pure strategy role. You will personally build the flagship systems - production agents, the context layer, the evaluation and safety stack - while also deciding what gets built, in what order, and to what standard. You will work across R&D, IT, Security, BizOps, Customer, Sales, Marketing, People, and Finance, with the AI Transformation Officer owning the business portfolio.
What you'll do - strategy and senior function:
Own the technical strategy for our internal AI transformation: target architecture, model and vendor choices, build-vs-buy calls.
Shape the AI portfolio: which workflows to automate, in what sequence, with what success metrics - and push back when the wrong thing is being built.
Set and enforce the bar on ROI and security for every AI system: cost, adoption, cycle-time impact, access control, and auditability are reviewed per project, not after launch.
Act as the senior technical voice for internal AI: advise leadership, review AI initiatives across the org.
Turn one-off builds into a real internal platform: reusable patterns, shared services, and standards that AI Champions and teams adopt.
Mentor AI Champions; raise the org's practical AI level.
Manage our AI- Champions Community.
Continuously evaluate models, vendors, and frameworks, and keep us agile.
What you'll do - hands-on build:
Personally design, build, deploy, and own the flagship internal agents and AI workflows, from prototype through production.
Build and evolve our AI context layer: connectors, APIs, retrieval services, metadata, identity and authorization, controls, and tool interfaces.
Create safe action tools for agents and infrastructure for agents to work.
Build RAG, tool-calling, structured-output, and multi-step agentic workflows using the right model and architecture for each problem.
Own the production quality bar for what you ship: evaluation datasets and automated checks (accuracy, groundedness, task completion, safety, cost etc.)
Work directly with business owners to map processes, test with users, and drive each pilot to a clear scale-or-stop decision.

our company's platform runs inside the security stacks of enterprise customers, processing live detection and telemetry data from SIEM, EDR, and SOAR systems at real scale. Until now, infrastructure has been built and carried by the founding engineering team alongside everything else. You will be the first dedicated owner of it. That means designing our company's cloud foundation across GCP and AWS, building the deployment, scaling, and observability layers from the ground up, and setting the standards the rest of engineering builds on. It also means holding a bar that most startups can defer and our company cannot: we sell to security teams who audit their vendors seriously, so how we run our own infrastructure is part of how we win. This is a hands-on senior role with real architectural authority, and as engineering grows you become the person others learn cloud and infrastructure from.
What you'll be doing
Multi-cloud architecture: Own the design and implementation of our company's multi-cloud architecture across GCP and AWS, including the cross-cloud standards, boundaries, and tradeoffs that keep two providers from becoming twice the complexity.
Infrastructure as code: Build and maintain the entire environment in Terraform, so infrastructure is reviewable, reproducible, and scales without a person in the loop.
Deployment pipelines from scratch: Create the CI/CD foundation that lets a small team ship quickly and safely, with the testing, gating, and rollback paths that make fast releases boring rather than risky.
Kubernetes at data scale: Run and scale the Kubernetes and containerized workloads behind our company's security data processing, where volume, latency, and cost pressure all show up at once.
Security of our own stack: Own identity, access, secrets, network boundaries, and infrastructure hardening to a standard that holds up under customer security review.
Reliability and visibility: Build the logging, monitoring, and alerting layer that tells us something is wrong before a customer does, and make on-call sustainable as the system grows.
Technical direction: Partner directly with the founding engineering team on architectural decisions, and raise the infrastructure and cloud fluency of every engineer who joins after you.

Were looking for a strong Senior SW Engineer to join our Data team.

EDR protects the endpoint. we protect everything else, anticipating and disrupting active attacks across the modern stack and stopping them before they reach the business.

our company is the leader in Zero-Impact Breach Prevention, delivered through Agentic Runtime Security for the modern infrastructure - cloud, SaaS, identity, and AI. By delivering the runtime detection and response that security teams already rely on for endpoints, extended to the infrastructure endpoint tools cant see, our company provides Zero-Impact Breach Prevention, in runtime.

The Role

Our detection engine is a large Python codebase running PySpark over cloud, SaaS, and identity data at scale on Databricks. Youll own its engineering quality - hardening whats there and extending it - working alongside the cyber investigators who define what it should catch.

In this role, you will:

Harden and extend a large production Python codebase: refactoring, module and API design, typing, test coverage.

Own and optimize critical PySpark workflows processing massive security datasets across multi-cloud environments - correctness and cost both.

Design the data models and queries the engine depends on, and fix the slow ones.

Work directly with cyber investigators to turn attacker behavior into detection logic that holds up in production.

Build with AI as a core tool: AI coding agents daily, and the AI tooling the team runs on - agentic and automated workflows (n8n or similar) and LLM-backed services on Bedrock or vendor APIs.

Raise the bar on engineering practice - code review, CI, and the standards the team holds each other to.

Contribute to architectural decisions and technical direction as the platform evolves.

we are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless company ronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

we are looking for a AI Transformation Engineer.
You'll join the AI Platform team, the group building the AI infrastructure, agentic workflows, and tooling that power R&D organization. Our mandate is to make the entire software development lifecycle faster and smarter with AI, and we're a small, hands-on team that ships real systems, not pilots
This isn't a role where work arrives as tickets, and it isn't a prompt-engineering role. You'll own a set of KPIs the team is driving, and it's on you to turn a fuzzy goal into something measurable: figure out what actually needs to be built, define how you'll know it's working, and build it. That means talking to engineers, understanding where the SDLC breaks down, and turning that into agentic workflows and platform capability that ship and hold up over time.

We are looking for a Senior Software Engineer to help build the core of our AI security platform and AI Control Plane.

You will build systems that help enterprises understand, monitor, and control how AI agents and LLM-based applications operate across their organization. We expect engineers to work in an AI-native way: leveraging and orchestrating AI agents to accelerate design, implementation, testing, debugging, and iteration, while owning the quality of the final result.

This role requires strong ownership, system design judgment, and the ability to turn complex AI-security problems into reliable production systems.

What you will do
Leverage and orchestrate AI agents and LLMs to design, build, test, debug, and ship core platform capabilities.
Build systems for monitoring, governing, and controlling AI agents, LLM applications, tool usage, prompts, context, policies, and runtime behavior.
Turn ambiguous AI-security problems into simple, scalable, production-grade systems.
Own technical decisions from design through production, working closely with Product, Security, and Engineering.

Were looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
Youll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. Youll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
Develop and improve our core algorithms.
Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices-not just standalone prototypes.
Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.

As an AI Engineer at our company, you will own the intelligent decision-making pipelines that turn complex workspace telemetry into autonomous security actions. You will design, build, and deploy the autonomous reasoning workflows and advanced data classification systems that drive our company's preventive operating model. Your focus will be on creating resilient, production-grade AI systems capable of deep policy comprehension, real-time prevention at the point of adoption, and autonomous remediation of existing risks.
This is a ground-floor opportunity to shape the AI strategy of a fast-growing cybersecurity company alongside a lean, elite team of builders.
WHAT YOULL DO
End-to-End Ownership: Own our AI capabilities entirely from initial research, architectural design, and prototyping, through to production deployment, optimization, and continuous monitoring.
Design & Build Agentic Workflows: Architect multi-step AI agents capable of autonomously investigating workspace risks, interpreting complex enterprise policies, and taking precise remediation actions.
Integrate Multi-Faceted ML: Bring innovation and creative thinking to our core engine. Implement diverse ML models across our entire research and product pipeline-utilizing clustering, text extraction, document analysis, and tabular data classification.
Ship Production-Grade AI: Build high-throughput, resilient, and fault-tolerant production code. You will ensure our AI pipelines and agentic workflows are highly predictable, deeply observable, and built to scale under enterprise-grade loads.
Implement Guardrails & Evaluation: Build continuous evaluation frameworks to benchmark agent accuracy, mitigate hallucinations, and enforce strict data security/privacy guardrails.

Required Staff Engineer - Office of the CTO
As a member of the Office of the CTO, your mission is to make every engineer 10x more effective. You will be a central driver in reimagining our Software Development Lifecycle (SDLC), building the intelligent platforms, automated golden paths, and AI-powered tools that will fundamentally change how we build software. This is a rare opportunity to have a massive, force-multiplying impact on our entire R&D & Product organizations, building the systems that enable hundreds of engineers to innovate faster and deliver with exceptional quality. The role is based in our Tel Aviv office.
Key Responsibilities:
Shape the Software Factory: Act as a key thought leader in shaping our Software Factory - the agentic workflows and loops that carry work from idea to production. You will research, prototype, and build the agents, harnesses, and tools behind them, and define the governance, controls, and standards that keep the factory predictable and safe at scale.
Build the Platform Beneath It: The factory only runs on solid foundations, and the Office of the CTO owns them. You will take a hands-on role in building and scaling the shared infrastructure every agentic workflow depends on - our AI gateway, shared memory and context graphs, guardrails, and the golden paths that abstract this complexity away from the teams using it.
Make Quality Automatic: Turn those standards into enforcement - automated gates in our CI/CD pipelines, evaluations and guardrails for agent-generated work, and the telemetry that tells us whether the factory is actually producing better outcomes. Quality, testing, security, and reliability become the default, not the exception.
Drive Adoption & Foster Community: Champion the tools you build. You will create documentation, lead workshops, and act as an internal advocate for your users, turning their feedback into the next iteration. You will also mentor and guide builders across R&D and product - in Tel Aviv and across our global sites - helping to foster our AI-native culture.