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Requirements:
What You Bring
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related engineering discipline, or equivalent practical experience.
5+ years of professional software engineering experience, including substantial experience building backend systems, distributed systems, or data-intensive applications in production environments.
Strong Python engineering skills, including modular architecture, dependency management, testing practices, observability, and production-grade code quality.
Strong SQL and data modeling expertise, including schema design, indexing strategies, event-driven data models, and scalable analytical aggregations.
Hands-on experience building incremental and idempotent data pipelines that handle duplicate, invalid, and late-arriving events without impacting downstream consumers.
Experience with at least one major cloud platform (Azure, GCP, or AWS) and modern lakehouse technologies such as Databricks, Delta Lake, Spark, or equivalent architectures.
Experience with streaming or messaging technologies such as Kafka, Pub/Sub, Event Hubs, or similar event-driven systems.
Proven ability to diagnose and resolve production data issues, including data quality problems, schema evolution, backfills, replay scenarios, and performance bottlenecks.
Strong written and verbal communication skills in English and experience collaborating effectively with globally distributed teams.

Nice to Have
Experience building industrial, IoT, manufacturing, or operational data platforms.
Familiarity with Digital Twin architectures and industrial data models.
Experience with graph databases, context graphs, knowledge graphs, or relationship-centric data modeling.
Exposure to AI/LLM-powered applications, including retrieval-augmented generation (RAG), agents, tool calling, or evaluation frameworks.
Experience working with Databricks or similar lakehouse platforms from both application and platform perspectives.
Experience building data products that directly support AI agents, intelligent applications, or machine learning workflows.

Requirements:
6+ years system level development experience
3+ years macOS development experience
Ability to demonstrate proficiency with AI Driven Development
Strong Swift skills development skills
Objective-C proficiency - maintain and refactor existing code
Deep knowledge of macOS frameworks
Experience with LaunchDaemons/LaunchAgents, code signing, packaging, notarization and privileged operations
Understanding of networking technologies and programming
Experience with Git, CI/CD
Comfortable with cross-platform development
Computer Science University degree or equivalent
Nice-to-Have
Browser extension development (Safari, Chrome)
Knowledge of AI/LLM tools (VS Code extensions, Claude Desktop)
Experience with Model Context Protocol (MCP)
Network traffic inspection or packet analysis background
Endpoint Security Framework or macOS security APIs
Python proficiency
Windows development experience
Rust experience.

Requirements:
B.Sc. in Electronics Engineering is a must
Deep understanding of electronic design and DFM - must
Understanding multidisciplinary products design
At least 5 years of experience with worldwide compliance / regulations - must
Knowledge in electronics components, PCB and PCBA design and assembly process, batteries, radio frequency (RF), IR, testing methods, and knowledge in ATE (Automatic Test Equipment)
At least 5 years of experience in board design with Altium or OrCAD - Advantage
Communication skills verbal, written, and presentation skills in both Hebrew & English is must.
Experience in matrixed worldwide organization and ability to work well in a team environment including cross-culture.
Self-motivated and organized.
Ability and willingness for hands-on assignments
Required Skills:
Accountability, Communication, Electrical Engineering

Requirements:
Your Knowledge & Skills
5+ years of experience as a threat researcher, incident responder, malware analyst, detection engineer or other relevant roles.
Practical experience in tracking state-sponsored or advanced financially motivated actors - including malware, infrastructure and TTPs.
Profound knowledge and understanding of malware and common attacking techniques.
Hands-on experience in automating and optimizing hunting and enrichment processes using code (preferably Python).
Familiarity with query languages and data exploration tools.
Ability to translate technical findings into actionable detection and prevention signatures.
Experience in writing technical blog posts and technical analysis reports.
Experience in public speaking and presentation of research in cyber security conferences .
Fluent English.

Requirements:
What You Bring
5+ years of professional software engineering, MLOps, or ML platform engineering experience in production environments.
Significant experience building or owning production ML infrastructure and lifecycle systems.
Strong Python engineering skills with production-grade architecture, modular design, testing, packaging, and robust error handling.
Strong understanding of the end-to-end ML lifecycle including training, deployment, monitoring, retraining, reproducibility, and lineage.
Experience working with large-scale data platforms such as Databricks, Spark, Delta Lake, or equivalent ecosystems.
Experience with ML platform and MLOps frameworks such as MLflow, Metaflow, Kubeflow, or equivalent ML lifecycle-management systems.
Proven ability to design reusable workflow orchestration using Airflow, Metaflow, or Databricks, covering automation, scheduling, dependency management, and production reliability.
Familiarity with operational patterns for LLMOps, AgentOps, and production AI systems.
Strong written and verbal communication skills in English.

Nice to Have
Experience with industrial, IoT or manufacturing platforms.
Experience with feature stores, model registries, dataset versioning, and lineage systems.
Experience with AI agents, RAG systems, production GenAI applications, or evaluation frameworks.

Requirements:
3+ years of Hands-On deep experience with AWS and its managed services (including ECS/EKS)
3+ years of programming experience using NodeJS and additional high-level programming language
Experience in PostgreSQL/ElasticSearch/Redis/SQS/Kafka - must
Experience with micro-services architecture - must
Highest sense of ownership
Excellent communication and interpersonal skills
Achievement and striving for excellence
Flexibility, versatility and creative thinking.

Requirements:
5+ years of experience as a Data Scientist
1-2 years of hands-on experience building GenAI or LLM-based applications
Strong proficiency in Python and data science and ML engineering best practices
Experience building and evaluating scalable distributed AI systems and cloud-native applications
Hands-on experience with GenAI technologies such as LangChain, LangGraph, MCP, RAG systems, vector databases, and agent frameworks
Experience integrating LLMs and AI services into production environments
Experience with Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS, Azure, or GCP - strong advantage
Strong understanding of APIs, software architecture, and system design
Familiarity with networking and cybersecurity domains - strong advantage
Strong ownership, curiosity, and ability to work in fast-moving environments
MSc in Computer Science or related field - advantage.

Requirements:
You Will Bring
6+ years of professional software engineering experience.
Strong Python development skills.
Strong backend engineering background, including APIs, services, integrations, or microservices.
Proven experience designing, shipping, or operating LLM-powered applications or GenAI systems.
Experience with RAG, AI agents, tool/function calling, prompt orchestration, evaluation, and observability.
Experience with microservices, distributed systems, and production backend architecture.
Strong understanding of system design, reliability, security, scalability, latency, and maintainability.
Ability to work with complex data models and expose them safely through AI systems.
Ability to operate in ambiguous technical areas and turn prototypes into production-ready systems.
Strong communication skills and ability to explain technical decisions clearly.

Preferred Qualifications
Experience with LangChain, LangGraph, or LangSmith.
Experience with Go, MongoDB, Databricks, Kubernetes, Docker, REST APIs, vector search, or event-driven systems.
Experience with Azure OpenAI, Gemini, or similar LLM platforms.
Experience building or using knowledge graphs, GraphRAG, or graph databases such as Neo4j.
Experience building multi-agent systems or orchestrating multiple specialized agents/tools in production.
Experience building internal platforms, SDKs, developer tools, or shared engineering infrastructure.
Experience with authorization, data segregation, multi-tenant systems, or user-level data scoping.
Experience in industrial, IoT, predictive maintenance, manufacturing, or operational-data domains.

Requirements:
2+ years of experience leading software development teams
Extensive experience designing and developing large-scale software systems
Solid networking knowledge
Experience in cybersecurity, threat prevention, network security, or related domains - a significant advantage
Self-driven, innovative, and passionate about technology.
Very high technical competence with approximately 50% hands-on involvement.

Requirements:
Advanced degree (M.Sc. or Ph.D.) in Physics, Electrical Engineering, Computer Science, Mathematics, or a related quantitative field.
Over 4+ years of experience, demonstrated expertise in DSP-based methods (e.g., STFT, FFT, Hilbert, Wavelet, etc.), to extract features and anomalies from the data.
Over 2 years of professional experience in ML/AI, with a proven track record in modeling and analyzing complex time-series data.
Proven technical leadership experience in large-scale, multidisciplinary projects.
Hands-on experience working with real-world physical data and sensor signals.
High proficiency in Python for the full development lifecycle, including model deployment and performance monitoring.
Ability to independently research, architect, and implement innovative technological solutions.
Prior experience in the industrial or manufacturing sector is advantageous.
Capability to translate theoretical research into robust, scalable, and production-ready systems.
Experience thriving in agile environments with a focus on rapid iteration, continuous feedback, and professional growth.
Excellent communication skills for collaborating with cross-functional teams, including Product Management and Engineering.

Requirements:
3+ years of experience as a data, product, or business analyst, ideally at a fast-growing consumer company
A degree in Industrial Engineering, Mathematics, Statistics, or a related field
Strong SQL skills and hands-on experience with BI tools (Looker, Tableau, Mixpanel, or similar)
Familiarity with A/B testing best practices and comfort working with complex datasets
The ability to work fluidly across product managers, data engineers, and data scientists, and to communicate technical findings clearly in English
Ready to work in an office environment most days of the week

Requirements:
Advanced degree (M.Sc. or Ph.D.) in Physics, Electrical Engineering, Computer Science, Mathematics, or a related quantitative field.
Over 4+ years of experience, demonstrated expertise in DSP-based methods (e.g., STFT, FFT, Hilbert, Wavelet, etc.), to extract features and anomalies from the data.
Over 2 years of professional experience in ML/AI, with a proven track record in modeling and analyzing complex time-series data.
Proven technical leadership experience in large-scale, multidisciplinary projects.
Hands-on experience working with real-world physical data and sensor signals.
High proficiency in Python for the full development lifecycle, including model deployment and performance monitoring.
Ability to independently research, architect, and implement innovative technological solutions.
Prior experience in the industrial or manufacturing sector is advantageous.
Capability to translate theoretical research into robust, scalable, and production-ready systems.
Experience thriving in agile environments with a focus on rapid iteration, continuous feedback, and professional growth.
Excellent communication skills for collaborating with cross-functional teams, including Product Management and Engineering.