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Requirements:
8+ years of experience in cybersecurity research, including 1+ years leading technical teams.
Experience managing, mentoring, and developing high-performing security researchers.
Hands-on coding experience (e.g., Python).
Experience with SQL or similar query languages for large-scale data analysis.
Excellent communication and stakeholder management skills.
Preferred Qualifications
Experience with AI, machine learning, or LLM-based security solutions.
Experience with big data platforms (e.g., GCP/BigQuery).
Experience with XDR, EDR, NDR, or CDR products.
Experience in email security research, detection engineering, or behavioral analytics.

Requirements:
Your Experience
6+ years as DevOps engineer with a passion for technology, strong motivation and responsibility
Proficiency in DevOps and Platform Engineering with expertise in AWS, GCP, Terraform, ArgoCD, Kubernetes, and related tools.
Experience in developing and maintaining CI/CD pipelines for continuous delivery in agile environments.
Skilled in managing cloud infrastructure, particularly with AWS and GCP, and adept in infrastructure as code practices using Terraform/Terragrunt.
Demonstrated capability in supporting high-scale SaaS applications, focusing on scalability, reliability, and performance.
Strong communication, strategic thinking, and problem-solving skills.
Experience with FinOps & Cost Management is considered an advantage.

Requirements:
Basic Qualifications
- High school or equivalent diploma.
- 1+ years of electrical or mechanical experience.

Preferred Qualifications
- Experience leading and managing operations of critical facilities which require in-depth knowledge in electrical, mechanical, and control systems.
- Knowledge of generic mechanical room infrastructure such as chillers, cooler units, and fan controls.
- Knowledge of mechanical systems (Mechanical, HVAC systems, Controls).
- Knowledge of key electrical competencies and theory.
- Electrical or mechanical journeyman license.

Requirements:
Experience: 5+ years of experience in product management within cybersecurity, data analytics, or enterprise B2B SaaS.
Leadership Mindset: A proven track record of taking full accountability for a product domain and delivering results in a high-velocity environment.
Communication & Influence: You must have high-level collaboration skills to effectively lead and align on product deliverables across Engineering, Product peers, Field, and executive-level customer teams.
Strategic Market Insight: Ability to synthesize market dynamics and customer pain points into a unique product vision, ensuring the delivery of high-impact capabilities that secure a clear competitive advantage within the SecOps ecosystem.
Technical Foundation: BSc/MS in Computer Science, Engineering, or equivalent technical/military experience. ​ Demonstrated experience leveraging modern AI methodologies and developer workflows, such as Vibe Coding and AI-assisted engineering, to accelerate prototyping, validate technical feasibility, and scale innovation.

Requirements:
Your Experience:
5+ years operating production cloud infrastructure, with a strong reliability focus (SRE, or DevOps/platform engineering with reliability ownership).
Deep hands-on experience with Kubernetes, Helm, ArgoCD, Terraform, and CI/CD.
Experience defining and operating SLIs, SLOs, and error budgets - or a clear grasp of the discipline and the drive to establish it from scratch.
Strong observability and alerting experience in Datadog or comparable platforms, including raising signal-to-noise in production.
Proven incident-response instincts - comfortable owning high-severity incidents and a genuine believer in blameless postmortems.
Proven ability to own platform and reliability projects end-to-end, from design through production operation and ongoing improvement.
Strong troubleshooting across distributed systems, Kubernetes, CI/CD, and live incidents.
Collaborative mindset - comfortable working across engineering, security, product, and leadership.
Comfort in a fast-paced, high-ownership environment where priorities shift but production quality doesn't.
Genuine interest in applying AI, automation, and intelligent workflows to operational work - and in building and supervising agents, not just using them.
Ownership-driven - You take responsibility for the reliability of the systems you build and operate, from SLO definition through incident command and continuous improvement.
Reliability as engineering - You treat reliability as a software problem to be solved with code, measurement, and automation - not an ops queue to be worked by hand.
Collaboration - You work effectively across engineering, security, product, and leadership to align on reliability priorities and drive shared outcomes.
Innovation balanced with pragmatism - You actively explore new approaches, particularly AI-assisted operations and agent supervision, while weighing them against reliability, maintainability, and operational simplicity.
Security mindset - You design and build with least privilege, auditability, and production safety as foundational principles rather than afterthoughts.
Clear communication - You articulate reliability, risk, cost, and security tradeoffs precisely to both technical and non-technical stakeholders.

Requirements:
A proven track record as an experienced architect who leads by example, capable of writing core, production-critical code, conducting rigorous architecture reviews, and mentoring senior technical staff.
At least 3 years of hands-on experience building, scaling, and optimizing Agentic LLM systems. This includes deep familiarity with common frameworks (e.g., LangChain, LangGraph, AutoGen) and a sophisticated understanding of tool-use optimization, memory management, and deterministic behavioral alignment.
At least 5 additional years of experience as a software engineer with high proficiency in Python and querying languages. You possess deep knowledge of software design patterns, concurrency, CI/CD pipelines, and writing clean, scalable, maintainable code.
BSc in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a related field from a top university. An advanced degree is highly preferred.
Demonstrated expertise in deploying and operating ML/LLM workloads at enterprise scale. You have a comprehensive understanding of containerization (Docker, Kubernetes), high-throughput data pipelines, and proactive system monitoring.
Exceptional ability to run end-to-end research-to-production initiatives, showcasing an analytical mindset capable of translating raw data into highly accurate, real-time defensive actions.
Extensive, hands-on experience utilizing, fine-tuning, and optimizing open-source generative AI frameworks and libraries (e.g., Hugging Face, PyTorch, vLLM, DeepSpeed) to build and deploy high-performance models.
Proven experience architecting data solutions and working within major cloud and big data ecosystems (e.g., GCP, AWS, Snowflake) to ingest, process, and analyze high-velocity, petabyte-scale datasets.
Outstanding communication and presentation skills, with a demonstrated ability to align cross-functional engineering, product, and research teams toward unified technical goals

Requirements:
4+ years in security engineering, cloud operations, incident response, threat hunting, DevSecOps, or related security disciplines.
2+ years of hands-on experience securing or operating environments within Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Infrastructure (GCI/GCP).
Strong experience with Kubernetes, containers, and modern cloud-native architectures.
Deep understanding of cloud networking concepts, including VPC Flow Logs, Transit Gateways, service meshes, and traffic patterns (East-West vs. North-South).
Strong comprehension of identity systems (IAM) and how policies interact with network controls to establish secure boundaries.
Experience with CI/CD pipelines and modern software delivery practices.
Proven experience with incident triage, investigations, containment, remediation, or formal operational response processes.
Experience with posture management, governance controls, or applying security best practices such as CIS Benchmarks and recognized cloud architecture frameworks.
Strong scripting or programming proficiency in languages such as Python, Go, or Bash.
Excellent communication skills, demonstrating the ability to clearly explain complex findings and facilitate alignment across cross-functional teams.
Preferred Qualifications
Experience working with large-scale data pipelines and analytics platforms such as BigQuery or Dataflow.
Familiarity with threat frameworks such as MITRE ATT&CK.
Recognized expertise or advanced certifications in cloud security, detection and response, or posture management across Amazon Web Services, Microsoft Azure, or Google Cloud.
Experience operating highly available, large-scale cloud environments.

Requirements:
7+ years of Product Management experience, with meaningful ownership of technical, platform, developer, AI, data, infrastructure, or enterprise products.
Proven ability to define product requirements, roadmaps, and execution plans in highly technical environments.
Strong understanding of modern AI technologies, including LLMs, AI agents, agentic workflows, RAG, AI evaluation, governance, and AI platform architectures.
Experience partnering hands-on with Engineering, Data, Infrastructure, Security, and Architecture teams.
Demonstrated success driving adoption of platform capabilities across complex, cross-functional organizations.
A senior individual contributor who can lead complex product domains from discovery through launch, adoption, and iteration.
A systems thinker who connects technology, product, governance, user experience, and business outcomes.
Able to influence senior stakeholders and technical teams without direct authority, especially in ambiguous, fast-moving environments.
Execution-oriented, structured, and comfortable moving between strategy, detailed requirements, prioritization, delivery, and adoption.
Focused on building reusable platform capabilities rather than one-off point solutions.
Advantage:
AI evaluation frameworks
Developer platforms and APIs
Cloud platforms
Enterprise SaaS
FinTech

Requirements:
Your Experience
8+ years of product management experience in cybersecurity or enterprise-grade cloud-based solutions, with at least 5 years leading high-performance PM teams.
Deep understanding of endpoint security, cloud workloads, and threat detection methodologies. Prior leadership experience in B2B/Enterprise product management at large organizations or innovative startups is highly preferred.
Proven track record of bringing complex, technical products to market at scale.
Strong business acumen with the ability to define pricing, packaging, and GTM strategies.
BS/MS in Computer Science, another engineering/technical degree, MBA, or equivalent military experience required.
Excellent analytical, written, and verbal communication skills. Ability to speak confidently in front of major customers and executives.
High proficiency in both English and Hebrew is required.
Your Attitude
Strategic and decisive leadership style with a strong sense of ownership and a take-charge attitude.
Pragmatic and direct-focused on driving results, solving problems, and removing blockers for your team.
Passionate about cybersecurity and inherently driven to design new, innovative solutions.
A collaborative team player with a "can-do" mentality who thrives in a highly dynamic, fast-paced environment.

Requirements:
5+ years of hands-on software engineering experience.
Proven experience designing and building large-scale, scalable cloud-based applications.
Expertise in microservices architecture, including technologies like Kubernetes, Docker, GKE, EKS, or AKS.
Experience with relational or NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB) and ORMs (e.g., JPA, Hibernate).
Bachelor of Science in Computer Science or equivalent practical experience.
Preferred Qualifications
Experience with Java or Kotlin with Spring Boot.
Familiarity with CI/CD pipelines and cloud infrastructure automation.
Experience with big-data architectures and technologies.
Knowledge of cybersecurity, information security, and software security principles.

Requirements:
At least 10 years software development experience, at least 2 years of experience as architect
Understands system engineering and architecture, proven experience in scale, system redundancy solutions, and performance
Experience in high-capacity and high-reliability products in multiple development languages and environments, multi-threading, and more
Experience with SQL Server
Understands Microservice, and event-based design and SAGA concepts
Error tolerance and zero data loss concepts
Experience delivering systems operated globally
Meaningful hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) - architecting and operating production workloads at scale
Experience architecting AI-enabled systems - LLM integration patterns, RAG, agentic/multi-step workflows, evals, and production concerns specific to AI (latency, cost, fallback, hallucination control)
AI fluent - daily, hands-on use of AI coding tools with clear judgment on when to trust output vs. rewrite it; goes beyond using AI to configuring it (custom instructions, rules, MCPs, tools); builds workflows and skills, not just prompts
Strong attention to business needs and converting them into working, effective products
Ability to adapt to various technologies and research new concepts
A true team player, able to both hold and drive a vision - and also to buy into it
Proactive attitude and does what it takes to get things done
Connected and updated in the latest & greatest of technology
A passion for technology!

Requirements:
Your Experience & Qualifications
Cybersecurity & Cloud Domain Expertise (SME-level): 7+ years of hands-on technical support, escalation, or enablement experience specifically with supporting cybersecurity or cloud security products.
Training and Enablement: Writing Technical Support Knowledge base articles, documenting troubleshooting playbooks and troubleshooting guides. Mentored Support engineers.
Analytical Skills: Proven ability to analyze support data and provide actionable insights
Advantage
Prompt Engineering: Proven expertise in systematically authoring, testing, and refining instructional inputs to drive specific model behavior.
Cloud Technologies: Experience with Cloud platforms like GCP, Kubernetes and containers
Experience with cloud cyber security technologies
AI/ML/LLM Foundation: Possesses a high-level understanding of AI tools, LLMs, machine learning, and applicative AI principles.
Technical Communication: Strong skills in translating observed model performance issues and Agentic Action requirements into clear, prioritized technical requirements for R&D teams.
Ownership: A dedicated individual who takes full responsibility for their work, driving projects to successful completion.