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Requirements:
You are a hands-on engineering leader with strong technical instincts and product intuition
You have built and scaled complex systems and still love writing code
You are excited by products that mix software and hardware in unpredictable environments
You enjoy collaborating across disciplines and take pride in shared wins
You move quickly, think clearly, and adapt well when things are still in flux
You believe in thoughtful architecture, fast feedback loops, and clear ownership
You see AI as a practical tool for improving speed, quality, and creativity.

Requirements:
8+ Years of Software Engineering
Exceptional track record of excellence (e.g., ex-8200 / academic distinctions / upwards career trajectory)
You care about creating the best customer experience
You share our view of the world as outlined above
Advantages:
Background in the IoT domain
You worked in a startup or a B2C company
Graduation from Electronics Engineering
Entrepreneurial Spirit: A track record of building from scratch.

Requirements:
Leadership experience: 3+ years as an Architecture Team Lead or Technical Lead.
Cloud-native expertise: Strong knowledge of Kubernetes architecture (preferably EKSA or SUSE) and container orchestration.
Systems knowledge: Good understanding of the Linux kernel, memory management, and I/O subsystems.
Networking: Solid understanding of L2-L7 networking, software-defined networking (SDN), and high-speed data planes.
AI & productivity tools: Familiarity with using LLMs and AI frameworks to enhance engineering workflows or product capabilities.
Architecture documentation: Proven ability to produce high-quality technical specifications and work within SAFe/Agile environments.
Telecom domain (advantage): Familiarity with the cellular stack (4G/5G NR, RAN, Core) or wireless communication protocols.
Networking functions (advantage): Experience with Virtual Network Functions (VNFs) or Cloud-Native Network Functions (CNFs).
System modernization: Experience modernizing complex systems while maintaining production stability.
Education:
Bachelors degree in Computer Science, Computer Engineering, or a related technical field.

Requirements:
Requirements:
1+ years of experience managing a team of DevOps engineers in an Agile environment.
5+ years of experience as a DevOps Engineer in cloud environment.
Proven production experience with deploying, running, and operating complex, high-availability, and large-scale web applications on AWS Cloud.
Deep expertise in Kubernetes, including different autoscaling strategies.
Terraform hands-on experience.
Proven production experience with monitoring and observability tools such as Grafana and Prometheus.
Deep understanding of scalable distributed systems and microservices architecture patterns.

Nice to have:
Good understanding and experience with database management.

Requirements:
BSc or MSc in Computer Science, Data Science, Machine Learning, Statistics, Electrical Engineering, or a related field, or equivalent practical experience.
5+ years of hands-on experience in applied machine learning, data science, search, natural-language processing, analytics engineering, or AI-enabled software systems.
Recent experience evaluating LLM, RAG, search, or agent systems using representative datasets, task-specific metrics, human review, failure analysis, and regression testing.
Strong Python and SQL skills, with experience building maintainable data pipelines, experiment workflows, services, or analytical tools.
Practical experience with several areas such as information retrieval, embeddings, hybrid search, reranking, classification, structured outputs, tool calling, or common LLM failure modes.
Strong statistical judgment, including sampling, leakage prevention, uncertainty, calibration, precision and recall, temporal drift, and controlled comparison of competing approaches.
Ability to work with semi-structured engineering data from issue-tracking systems, source control, code reviews, continuous integration, logs, dashboards, and test systems.
Clear communication skills and the ability to explain results, limitations, and tradeoffs to technical and business stakeholders.

Requirements:
Requirements:
4+ years of experience with microservices development.
Deep understanding of microservices architecture and cloud-native development.
Experience designing and building asynchronous systems and working with message queues/event-driven architectures.
Experience with databases.
Experience in developing a SAAS product.

Advantages:
Experience in developing a security-related SAAS product.
Experience with continuous integration and deployment (CI/CD).
B.Sc in Computer Science/Software Engineering or related field.
Familiarity with the GraphQL framework.
Familiarity with the Graph Databases.

Requirements:
You love solving problems for people, and you move fast when they need you.
You combine exceptional service with strong technical judgment.
You're AI first. Your instinct is to automate before doing something manually twice.
You enjoy building systems that scale instead of relying on heroics.
You care deeply about security without losing sight of usability.
You're highly organized, proactive, and comfortable owning an entire function.
You thrive in environments where you can roll up your sleeves, take ownership, and continuously improve how things work.

Requirements:
B.Sc. in Electrical Engineering, Computer Engineering, Computer Science, Telecommunications, or a related field, or equivalent practical experience.
5+ years of hands-on experience in RAN system verification, system integration, performance engineering, lab engineering, field support, or a similar technical role.
Strong knowledge of at least one RAN technology or domain, such as LTE, 5G NR, PHY, MAC, RLC, PDCP, protocol stack, RAN performance, or end-to-end feature validation.
Proven experience investigating complex technical issues using logs, traces, configurations, software versions, and test results.
Practical scripting experience using Python, Shell, or similar tools.
Strong analytical skills, technical ownership, attention to detail, and the ability to collaborate effectively across cross-functional engineering teams.
Excellent written and verbal communication skills.

Requirements:
Minimum Qualifications:
5 years of experience in cybersecurity, cyber-incident response, and threat hunting.
Experience with cloud security, including cloud services, Kubernetes, cloud architecture, and major cloud providers (AWS, GCP, Azure).
In-depth knowledge of OS internals for both Windows and Linux.
Experience with scripting languages and querying languages.
Full professional fluency (written and verbal) in both Hebrew and English.
Preferred Qualifications:
Strong analytical skills with a keen attention to detail.
Passion to solve any problem and to thrive as an independent and innovative Incident Responder.

Requirements:
Requirements
At least 3 years of experience as a team leader.
At least 8 years of hands-on experience in server-side development.
At least 2 years of hands-on experience with Go lang.
Proven experience in developing large-scale systems using cloud services and NoSQL databases (such as GCP orand AWS, Couchbase).
Experience with microservices architecture, Docker, and container. orchestration (e.g., Kubernetes) & CI/CD.
Experience with Agile methodology.
Ability to quickly understand complex systems and drive solutions.
Fluent in written and spoken English.
Excellent spoken and written communication skills.
Availability to work in a hybrid model - at least 3 days a week from the office.

Advantages
Direct experience with Ad Tech platforms (DSPs, SSPs, Ad Exchanges).
Experience with Vue.js or similar frontend frameworks.
Experience with Java.
Familiarity with observability tools and practices.
Experience working with remote teams.
Advanced knowledge of unit testing frameworks and mocking.
B.Sc. in Computer Science or equivalent.

Requirements:
An Architect Who Codes: You lead by example. You prefer writing a script to writing a Jira ticket.
Modern Stack Native: You live in the cloud and speak fluent Python. You know DBT and Airflow inside out.
IoT Ready: You are excited by the challenge of moving beyond standard transactional data into the messy, high-volume world of hardware and sensors.
Impact-Obsessed: You don't build technology for its own sake. You build it to solve real business problems and create products people love.

Requirements:
BSc or MSc in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related field, or equivalent practical experience.
Typically 7+ years of hands-on experience in production software, ML platform, search, data, or infrastructure engineering, including meaningful recent experience shipping LLM-powered systems; exceptional candidates with equivalent depth are welcome.
Strong Python engineering skills and experience designing maintainable APIs, services, libraries, and data pipelines. Experience with Go, Java, or C/C++ is an advantage.
Strong understanding of transformer-based language models and production inference, including tokenization, context management, batching, KV caching, parallelism, quantization, structured output, tool calling, and common model failure modes.
Demonstrated experience building production RAG or enterprise-search systems using embeddings, vector and/or lexical search, metadata filtering, reranking, source attribution, and systematic retrieval evaluation.
Experience defining task-specific LLM evaluations using representtive datasets, strong baselines, domain-expert review, automated metrics, human feedback, error analysis, and regression thresholds.
Experience deploying and operating containerized services on Linux using Docker and Kubernetes or an equivalent orchestration environment.
Practical experience with GPU-backed model serving, performance profiling, capacity planning, monitoring, and reliability engineering.
Strong knowledge of distributed-system fundamentals, authentication and authorization, API security, secrets handling, encryption, auditability, and data lifecycle controls.
Experience with Git, automated testing, CI/CD, infrastructure as code, observability, and production incident response.
Sound technical judgment about quality, security, maintainability, hardware efficiency, and total cost-not just model benchmark scores.
Ability to lead an ambiguous, cross-functional initiative, explain complex AI behavior in plain language, and help other teams ship safely on a shared platform.