דרושים » תוכנה » Senior Software Engineer

We are looking for a Senior Backend AI Developer to architect and build the intelligent backbone of our platform: the multi-agent investigation pipeline, the data layer that consolidates signals from cloud providers and security tools, and the orchestration layer that ties it all together. You will work at the intersection of backend engineering and applied AI, designing systems that are durable, scalable, and smart enough to reason about complex cloud security environments.
What You'll Do:
Design and build the backend services that power Copperhelm's AI-driven investigation and remediation engine.
Architect and evolve a multi-agent system where specialized AI agents collaborate to investigate, prioritize, and resolve cloud security findings autonomously.
Build and optimize agent workflows using the Claude Agent SDK and Temporal.io for durable, orchestrated execution of long-running investigations.
Design and maintain the LLM inference pipeline, including prompt engineering, output validation, and integration with Amazon Bedrock.
Collaborate with product and engineering to translate threat models and investigation playbooks into automated agent workflows.
Make critical architectural decisions on agent boundaries, coordination patterns, data models, and scaling strategies.

Were looking for a senior engineer who is ready to move beyond traditional software development and take full ownership of product delivery.

This is not a classic backend engineering role. You will work directly with Product to turn ideas and requirements into production-ready solutions, make the technical decisions, and use AI coding agents as a core part of your engineering workflow.

You will own the outcome, not just the code. AI-generated code is your code, and you are accountable for its quality, security, performance, and maintainability.


Own product features end-to-end, from requirements through production and monitoring.
Work with Product to translate business problems into clear technical solutions and execution plans.
Design scalable and maintainable solutions, including architecture, APIs, data models, and integrations.
Use AI coding agents to accelerate implementation, testing, debugging, and automation.
Review and improve AI-generated code, ensuring high standards for correctness, security, performance, and maintainability.
Make independent technical decisions and communicate architectural trade-offs clearly.
Contribute across backend, frontend, QA, CI/CD, and infrastructure when needed to deliver a feature.
Investigate and solve complex production and performance issues.
Continuously improve how we build, test, and ship software using AI-native development practices.

At Check Point, what you do matters. Every day, we protect over 100,000 organizations worldwide from increasingly sophisticated cyber and AI-driven threats, securing their AI transformation.
Our prevention-first approach safeguards hybrid networks, cloud environments, digital workspaces, and AI systems, stopping attacks before they happen.
This is where innovation meets real-world impact. Youll help customers across industries operate with confidence in a rapidly changing digital world, working alongside smart, curious people who take ownership, challenge assumptions, and solve complex problems.
Were proud to be recognized by TIME, Newsweek, and Forbes for excellence and workplace culture.
What really sets Check Point apart is the opportunity to grow, contribute, and help companies navigate their AI transformation securely.
If youre excited to work at the forefront of AI-driven security on a global scale, this is the place to do it.
Key Responsibilities
About the Team
We are looking for a passionate Full Stack Engineer to join our team, building modern, scalable, and intuitive web applications.
Our team owns user-facing capabilities end-to-end, from rich React experiences and frontend architecture to API integrations and supporting backend services. We work closely with product, UX, platform, and engineering teams to turn complex requirements into simple, reliable, and high-quality solutions.
AI is an integral part of how we work. We are looking for engineers who know how to use modern AI tools effectively and responsibly throughout the development lifecycle, including problem-solving, coding, debugging, testing, and code review.
This is an opportunity to take ownership of meaningful features, influence technical decisions, and help shape both our product and the way our team builds software.
Your Impact & Responsibilities
Design, develop, and maintain scalable web applications using React and TypeScript.
Build and integrate APIs and backend services that support complex user-facing workflows.
Own features end-to-end, from requirements and technical planning through implementation, testing, deployment, and production monitoring.
Create reusable frontend components and contribute to a consistent and maintainable application architecture.
Work with API contracts, asynchronous data flows, error handling, authentication, and application state.
Improve application performance, usability, reliability, security, and developer experience.
Use AI-assisted development tools and workflows to accelerate delivery while maintaining high standards of correctness, security, and code quality.
Review and validate AI-generated outputs rather than relying on them blindly.
Collaborate closely with product managers, UX designers, backend developers, and other engineering teams.
Contribute to technical discussions, architecture decisions, code reviews, and engineering best practices.

We are looking for a Senior II Software Engineer to join the CX Platform team, the foundational engineering team powering our entire Customer Experience group. This is a high-impact, hands-on role at the intersection of backend engineering, AI infrastructure, and customer-facing product.

You'll work across the full platform stack (backend services, data pipelines, security, cost, and scale) with a meaningful and growing focus on AI infrastructure. We own the agentic platform for the entire Product Offering group: from building the LLM infrastructure and agentic workflows to ensuring they're reliable, observable, and safe in production.

What you'll be doing:
Own AI infrastructure for the Product Offering group. Design, build, and evolve the shared AI platform (agentic workflows, LLM integrations, observability, and guardrails) that CX product teams build on.
Ship agentic features end to end. Lead development of AI-driven capabilities using LangChain, LangFuse, and AWS Bedrock, from architecture through production deployment and monitoring.
Drive platform architecture. Set the technical direction for the CX backend (services, data pipelines, API patterns) with an eye for scalability, reliability, and developer experience.
Own core data foundations. Design resilient data-access patterns across Snowflake, Elasticsearch, Kafka, Redis, and MySQL; keep pipelines fast, fresh, and reliable.
Mentor and elevate. Help engineers across the CX group grow in backend craft, AI engineering, and system design thinking.
Collaborate cross-functionally. Work with product, design, and customer-facing teams to turn ambiguous problems into well-scoped, high-quality solutions.

We are looking for a Senior Full Stack Developer to own high-impact features end-to-end across our platform. You will build the backend services and APIs that power our security product, as well as the dashboards and investigation workflows our customers interact with daily. This is a foundational hire. You will shape both the product and the engineering culture as we scale.
What You'll Do:
Own features end-to-end: from architecture through implementation, testing, deployment, and iteration.
Design and extend backend services and APIs with NestJS, PostgreSQL, and Drizzle ORM.
Build data-dense frontend experiences using Next.js, React, and Tailwind CSS.
Collaborate closely with product and engineering to translate complex vulnerability data into intuitive, actionable workflows.
Help establish engineering best practices around testing, observability, CI/CD, and code quality as we grow the team.

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 Infrastructure Engineer who views "Infrastructure as Software." In 2026, we dont just manage servers; we build high-performance environments that allow multi-agent systems to operate at scale.
You will be a core member of the R&D team, blending deep DevOps expertise with the coding rigor of a Backend Engineer. You arent just "configuring" AWS; you are architecting the distributed systems and data pipelines that power our autonomous security brain. Your mission is to ensure that while our agents are evolving and taking actions, our underlying platform remains immutable, observable, and infinitely scalable.
What You'll Do
Design, build, and operate our company's cloud infrastructure using AWS, Kubernetes, and Infrastructure as Code.
Build internal tools and platform services using Python and Go to improve developer productivity and system reliability.
Own infrastructure automation with Terraform, Pulumi, and modern cloud-native tooling.
Partner closely with Backend, Data Science, and Security Engineering teams to build scalable, reliable platforms.
Improve observability, monitoring, and incident response across distributed production systems.
Design and optimize infrastructure for performance, scalability, security, and cost efficiency.
Help shape engineering best practices, platform architecture, and developer experience as our company continues to grow.

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.

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.