דרושים » אבטחת איכות QA » Quality Lead - AI-Native R&D

We are looking for a strong AI QA Automation Lead with high technical skills to build methodology, provide vision and direction, and lead quality & automation throughout the organization. This role operates at the intersection of traditional software quality and AI-powered development: owning quality not just for code written by humans, but for systems designed, generated, and executed by AI agents.
How will you make an impact?
Serve as the quality leader for an entire domain, accountable for product quality across human-written, AI-generated, and agent-executed software
Actively track the domains quality tasks, analyze the testing needs, and define QA activities and ownership
Continuously analyze and challenge existing testing coverage
Plan, develop, and execute test strategy across our Cloud Analytics platform , extending to AI-powered and agentic system components
Review and approve tests cases and complex system scenarios
Prepare and present data-driven quality insights, KPIs, risks, and trends to leadership through regular domain level reporting.
Lead domain-level RCA reviews for customer-facing bugs and major incidents; detect trends and drive systemic improvements.
Own post-deployment quality monitoring, drive sanity completion and rapid triage of production issues within the domain.
Lead Quality assurance efforts that align with product objectives to ship products rapidly and with confidence
Help the teams design the performance, concurrency and latency test scenarios and metrics
Lead risk-based test strategy with product and engineering - define acceptance criteria and quality gates that support high delivery velocity without sacrificing customer-impacting quality
Implement and evolve QA processes to get effective test signals and scale testing efforts across our services
Define acceptance criteria for non-deterministic systems using golden-set evaluation, behavioral contracts, and statistical confidence thresholds
Define adversarial and edge-case testing methodologies for AI features: prompt injection resistance, boundary condition handling, and graceful degradation
Partner with engineering and DevOps teams to leverage automation for scalable solutions to prevent regressions and ensure reliability of product
Track and improve CI/CD pipeline health across environments - expanding automation coverage, hardening test stability, and ensuring every environment produces trustworthy test signals.
Plan and lead NFR testing
Define guidelines and best practices for our automation and manual testing procedures
Coordinate cross-organization/cross-region testing activities.

We are looking for a visionary engineering leader to architect and lead our quality engineering discipline into the AI era. You will own automation infrastructure, performance engineering, and all AI-driven quality initiatives - building and mentoring high-performing teams of engineers and architects who set the standard for how modern software is validated at scale.
This is not a maintenance role. You will disrupt legacy testing paradigms, champion agentic test generation, and embed AI-quality thinking into every stage of the SDLC - from the first commit to production monitoring. You will be the organizational bridge between engineering velocity and product reliability.
How will you make an impact?
AI Quality Strategy
Define and lead all AI-augmented quality initiatives across the organization
Deploy LLM-powered test generation, triage, and root-cause analysis tooling
Build frameworks for validating AI/ML model outputs and non-deterministic behaviors
Evaluate and integrate emerging AI quality toolchains (GitHub Copilot, Claude, agents)
Champion responsible AI testing practices, coverage standards, and governance models
Automation Engineering
Own the full automation strategy across UI, API, and integration layers
Architect scalable, maintainable test frameworks
Drive shift-left testing embedded in CI/CD pipelines (Jenkins, GitHub Actions)
Establish coverage KPIs, flakiness SLAs, and automation ROI metrics
Lead adoption of self-healing and AI-assisted test maintenance
Performance Engineering
Lead performance and load testing strategy at enterprise scale
Define SLOs/SLAs and partner with SRE on reliability thresholds
Design continuous performance validation integrated into delivery pipelines
Drive root-cause analysis for regressions, bottlenecks, and latency spikes
Oversee toolchain including k6, JMeter, Gatling, and APM integrations
People & Org Leadership
Lead and grow teams of automation engineers, performance engineers, and architects
Set technical direction, OKRs, and career ladders for the quality engineering org
Hire senior ICs and architect-level talent; build succession depth
Foster a culture of ownership, engineering craft, and continuous learning
Partner cross-functionally with Product, DevOps, and R&D leadership.

We are looking for a visionary engineering leader to architect and lead our quality engineering discipline into the AI era. You will own automation infrastructure, performance engineering, and all AI-driven quality initiatives - building and mentoring high-performing teams of engineers and architects who set the standard for how modern software is validated at scale.
This is not a maintenance role. You will disrupt legacy testing paradigms, champion agentic test generation, and embed AI-quality thinking into every stage of the SDLC - from the first commit to production monitoring. You will be the organizational bridge between engineering velocity and product reliability.
How will you make an impact?
AI Quality Strategy
Define and lead all AI-augmented quality initiatives across the organization
Deploy LLM-powered test generation, triage, and root-cause analysis tooling
Build frameworks for validating AI/ML model outputs and non-deterministic behaviors
Evaluate and integrate emerging AI quality toolchains (GitHub Copilot, Claude, agents)
Champion responsible AI testing practices, coverage standards, and governance models
Automation Engineering
Own the full automation strategy across UI, API, and integration layers
Architect scalable, maintainable test frameworks
Drive shift-left testing embedded in CI/CD pipelines (Jenkins, GitHub Actions)
Establish coverage KPIs, flakiness SLAs, and automation ROI metrics
Lead adoption of self-healing and AI-assisted test maintenance
Performance Engineering
Lead performance and load testing strategy at enterprise scale
Define SLOs/SLAs and partner with SRE on reliability thresholds
Design continuous performance validation integrated into delivery pipelines
Drive root-cause analysis for regressions, bottlenecks, and latency spikes
Oversee toolchain including k6, JMeter, Gatling, and APM integrations
People & Org Leadership
Lead and grow teams of automation engineers, performance engineers, and architects
Set technical direction, OKRs, and career ladders for the quality engineering org
Hire senior ICs and architect-level talent; build succession depth
Foster a culture of ownership, engineering craft, and continuous learning
Partner cross-functionally with Product, DevOps, and R&D leadership.

We are looking for a visionary engineering leader to architect and lead our quality engineering discipline into the AI era. You will own automation infrastructure, performance engineering, and all AI-driven quality initiatives - building and mentoring high-performing teams of engineers and architects who set the standard for how modern software is validated at scale.
This is not a maintenance role. You will disrupt legacy testing paradigms, champion agentic test generation, and embed AI-quality thinking into every stage of the SDLC - from the first commit to production monitoring. You will be the organizational bridge between engineering velocity and product reliability.
How will you make an impact?
AI Quality Strategy
Define and lead all AI-augmented quality initiatives across the organization
Deploy LLM-powered test generation, triage, and root-cause analysis tooling
Build frameworks for validating AI/ML model outputs and non-deterministic behaviors
Evaluate and integrate emerging AI quality toolchains (GitHub Copilot, Claude, agents)
Champion responsible AI testing practices, coverage standards, and governance models
Automation Engineering
Own the full automation strategy across UI, API, and integration layers
Architect scalable, maintainable test frameworks
Drive shift-left testing embedded in CI/CD pipelines (Jenkins, GitHub Actions)
Establish coverage KPIs, flakiness SLAs, and automation ROI metrics
Lead adoption of self-healing and AI-assisted test maintenance
Performance Engineering
Lead performance and load testing strategy at enterprise scale
Define SLOs/SLAs and partner with SRE on reliability thresholds
Design continuous performance validation integrated into delivery pipelines
Drive root-cause analysis for regressions, bottlenecks, and latency spikes
Oversee toolchain including k6, JMeter, Gatling, and APM integrations
People & Org Leadership
Lead and grow teams of automation engineers, performance engineers, and architects
Set technical direction, OKRs, and career ladders for the quality engineering org
Hire senior ICs and architect-level talent; build succession depth
Foster a culture of ownership, engineering craft, and continuous learning
Partner cross-functionally with Product, DevOps, and R&D leadership.

We are looking for a Head of Forward Deployed Engineering to lead the team that turns our platform into live production systems inside the largest enterprises. You will own a team of 10+ FDEs (and growing), the delivery bar they are held to, and the outcomes they ship.
This is a player-coach role. You set the standard, build the system that enforces it, and stay close enough to the work to review a flow design and call out what's wrong.
What You'll Do
Own delivery outcomes. On-time go-lives, production stability and client satisfaction all land on your desk. You manage by data: delivery metrics, quality signals, escalation rates and client feedback.
Raise the bar. Run a clear performance system with a defined FDE bar, 30-60-90 ramps for new hires, and honest evaluations. Coach the people who can grow. Make the hard calls on those who can't.
Run the operating rhythm. Own the flow review forum, the deployment status cadence, and the on-site schedule across clients.
Grow the team. Own the hiring pipe, from sourcing through a practical interview loop to onboarding that gets new FDEs to independent delivery in 90 days.
Stay in the field. Show up at key clients, especially around go-lives. Your credibility comes from being in the room, not above it.

Forward Deployed Engineering sits at the frontier of that work. We embed with our most strategic enterprise customers, running on-prem, air-gapped, and heavily regulated environments, and own the technical arc from discovery through production.

Your day might include designing a solution architecture for a high-impact agentic use case, debugging a production routing issue deep in our codebase, deploying a new MCP server with a customer team, or aligning with a CTO on a rollout plan; all in service of outstanding outcomes and rapid time to value for the organizations we work with.

This is a founding function. The first FDEs will define how we conducts field engineering: the standards, playbooks, and technical bar. The work is hands-on, commercially scoped, and high-leverage: youll partner closely with Product, Research, Platform, Security, and GTM, and what you learn in production directly shapes what the core team ships next. If you thrive under ambiguity, make fast and sound decisions when the stakes are high, and get energy from closing the gap between what customers hit in production and what we build next, this role is for you.

In This Role You Will
Own technical delivery across multiple deployments from first prototype to stable production, including core engineering work that unblocks strategic accounts.
Lead deep technical diagnosis and remediation in our codebase and production environments, model routing and gateway behavior, large-PR scaling, multi-pass behavior, performance and reliability issues.
Build customer-specific extensions (MCP servers, agent skills, platform integrations) that deliver immediate value and become durable product capability; upstream them into the main roadmap where appropriate.
Embed closely with customer engineering teams: serve as their senior technical counterpart for hands-on debugging, architecture reviews, and joint implementation.
Scope work, sequence delivery, and remove blockers early; make deliberate trade-offs between scope, speed, and quality to protect delivery timelines.
Partner with Product, Research, and Platform to convert account learnings and eval-driven feedback into roadmap items, specs, and prioritized engineering work.
Codify working patterns into tools, playbooks, runbooks, and reproducible benchmarks that Support and account teams can operate independently and help scale the field engineering function across the organization.

We are looking for a Head of Forward Deployed Engineering to lead the team that turns our platform into live production systems inside the largest enterprises. You will own a team of 10+ FDEs (and growing), the delivery bar they are held to, and the outcomes they ship.
This is a player-coach role. You set the standard, build the system that enforces it, and stay close enough to the work to review a flow design and call out what's wrong.
What You'll Do
Own delivery outcomes. On-time go-lives, production stability and client satisfaction all land on your desk. You manage by data: delivery metrics, quality signals, escalation rates and client feedback.
Raise the bar. Run a clear performance system with a defined FDE bar, 30-60-90 ramps for new hires, and honest evaluations. Coach the people who can grow. Make the hard calls on those who can't.
Run the operating rhythm. Own the flow review forum, the deployment status cadence, and the on-site schedule across clients.
Grow the team. Own the hiring pipe, from sourcing through a practical interview loop to onboarding that gets new FDEs to independent delivery in 90 days.
Stay in the field. Show up at key clients, especially around go-lives. Your credibility comes from being in the room, not above it.

We are looking for a Staff QA Architect to join our elite team, contributing to the development of one of our strategic products for Cloud Security.
In your role, you will be responsible for developing and maintaining high-quality work procedures, leading the team to achieve better quality.
You will be involved in the full feature lifecycle - from gathering requirements with the product team to release - ensuring quality at every stage, including bug management and observability and defining QA work procedures, guidelines, and designing the entire testing process.
The ideal candidate will be responsible for leading quality activities across solution-level features, ensuring the highest standards of quality are met. This role requires a proactive individual with excellent leadership and communication skills, capable of driving quality initiatives and fostering a quality-focused mindset within the organization.
Comprehensive understanding of the product's functionality, target use cases, and the value it delivers to customers.
Lead the quality efforts across multiple interfaces with a proactive and assertive approach, serving as the key point of contact for all quality-related matters.
Define, implement, and manage QA processes, promoting a quality-driven culture and managing the quality backlog.
Define the overall testing strategy and tooling stack across integration, regression, and load/performance testing.
Drive an automation-first approach - collaborating with developers to ensure code is testable and observable.
Lead root-cause analysis for escaped bugs and define improvement actions.
Own CI quality metrics and dashboards (e.g., build stability, test flakiness, regression coverage).
Influence decisions regarding test environments, mocks, test data, and production parity.
Collaborate with the centralized quality group to ensure E2E test coverage.
Quality Point of Contact: Serve as the primary point of contact for quality-related matters concerning the unit or the cross-group feature.

we are looking for a Senior AI Engineer - Exploration & Prototyping.
That is this role. You take an open question, run a focused spike, and come back with numbers and a recommendation. You read the source of the frameworks you evaluate rather than trusting their marketing. You build prototypes to settle arguments.
You do not own a subsystem and you do not ship to customers, which is exactly what protects the work: exploration inside a delivery team always loses to the sprint. You sit alongside the platform, research, and forward-deployed teams, you borrow their context freely, and your output is evidence they can act on.
You will be trusted with real influence early. The recommendations you write become the architecture other people build against, so the bar is not a working demo but a defensible conclusion, including the ones that say no.
What Youll Do:
Run technical spikes that close open decisions, covering agent orchestration frameworks, real-time transport, memory protocols, agent interoperability standards, LLM selection and routing, evaluation harnesses, and the production library and stack choices underneath all of it.
Build prototypes to de-risk, standing up something real quickly, proving or disproving the thing in question, and moving on without becoming attached to the code.
Read and evaluate unfamiliar codebases, going into the source of a candidate framework to find out whether it can actually support what we need rather than what its documentation implies.
Design the measurements that make a decision defensible, building the harness, running the comparison, and reporting latency, cost, and failure behavior honestly.
Own build-versus-adopt recommendations for platform infrastructure, frameworks, and libraries, including a clear statement of what it would cost to be wrong.
Write the recommendation down. Every spike ends in a short, decisive document another engineer can act on, with the evidence, the rejected options, and the reasoning behind the call
Hand off cleanly, transferring what you learned to the team that will own the capability in production, and staying available while they pick it up.
Track the landscape across agentic infrastructure, real-time frameworks, and adjacent AI tooling, and bring forward the things that genuinely change what we can build.