We are looking for a hands-on Senior Applied AI Evaluation Engineer to improve how our RAN R&D organization analyzes engineering tickets, supports Root-Cause Analysis, recommends ownership, and learns from resolved cases.
You will build rigorous evaluation datasets, establish meaningful baselines, compare internal and approved external tools, analyze failure modes, and prototype improvements across retrieval, classification, prompting, agent workflows, and model selection.
This role is focused on measurable, evidence-based improvement rather than AI demonstrations. You will assess whether AI-generated conclusions are accurate, grounded in evidence, appropriately calibrated, and useful to engineering teams.
As a secondary area of focus, you will analyze engineering workflows at case and team level to identify bottlenecks, handoffs, dependencies, and opportunities for process improvement.
What you will do:
Define high-value RAN ticket-intelligence use cases, acceptance criteria, evaluation metrics, and quality guardrails.
Build and maintain representative, versioned evaluation datasets using resolved tickets, Root-Cause Analysis, logs, test evidence, code changes, reviews, reassignment history, and outcomes.
Establish current-tool and non-AI baselines before evaluating new LLM, RAG, search, or agent-based approaches.
Evaluate approved internal, commercial, local, and open-source solutions using secure and reproducible data-handling processes.
Measure retrieval quality, groundedness, diagnosis accuracy, citation support, routing recommendations, calibration, abstention, latency, cost, and human effort.
Design held-out, time-based, edge, and adversarial test cases while preventing data leakage and future-outcome contamination.
Analyze failures and turn incorrect conclusions, misrouting, unsupported claims, and missed evidence into prioritized improvements.
Prototype improvements in search, metadata, context construction, prompting, reranking, classification, agent workflows, and model selection.
Develop reusable evaluation pipelines, tools, services, APIs, dashboards, or documented workflows.
Work closely with AI, RAN, QA, System Integration, Release, Field, data, and engineering teams to review results and support evidence-based decisions.

We are looking for a hands-on technical leader to build and operate a secure local large-language-model platform for the company. The platform will allow engineering and business teams to use generative AI with proprietary source code, product documentation, technical standards, test artifacts, support knowledge, and other approved internal data while keeping sensitive information within company-controlled environments.
This is a senior individual-contributor role spanning applied LLM engineering, platform architecture, search and data pipelines, security, and production operations. You will turn promising prototypes into a dependable internal capability: selecting and optimizing open-weight models, building permission-aware retrieval, creating reusable APIs and tools, integrating with existing engineering workflows, and establishing objective ways to measure quality, safety, latency, capacity, and business value.
The successful candidate will understand that a useful enterprise LLM is more than a model and a chat interface. It requires trustworthy source grounding, strong access controls, repeatable evaluation, careful tool permissions, observable production services, and an operating model that keeps data, indexes, prompts, models, and dependencies current. You will make pragmatic build-versus-buy decisions and choose the simplest approach-search, retrieval-augmented generation (RAG), prompting, workflow automation, or model adaptation-that meets each use case.
Initial use cases may include engineering knowledge discovery, source-code understanding, troubleshooting assistance, technical-document Q&A and summarization, test and log analysis, and drafting structured engineering artifacts. The platform should be extensible to additional approved use cases as needs and model capabilities evolve.
What you will do:
Own the architecture and technical roadmap for a secure, reliable, and maintainable local LLM platform deployed in our controlled infrastructure.
Partner with engineering, product, support, IT, information security, legal, and domain experts to prioritize high-value use cases and translate them into measurable product and platform requirements.

We are looking for a hands-on wireless systems engineer to lead the development of a multi-RAT digital twin for our Open RAN solution. The digital twin will execute production RAN software-beginning with scheduler and MAC behavior-in a closed loop with PHY, channel, UE, traffic, and network models. It will allow engineering teams to design, evaluate, and compare features for LTE, 5G NR, and 2G without requiring a dedicated physical radio setup for every development cycle.
This is a senior individual-contributor role at the intersection of wireless systems, simulation, production software, and AI/ML. You will evolve an existing LTE end-to-end simulator into a scalable engineering platform for feature development, regression testing, performance optimization, and evidence-based pre-validation. Initial use cases include MAC scheduler and link-adaptation improvements, power control, mobility and interference scenarios, and neural-network-assisted channel estimation.
The successful candidate will understand that a useful digital twin must be both fast and trustworthy. You will define multiple fidelity levels-from rapid surrogate models to full PHY processing-and establish repeatable methods for calibrating the twin against lab or field reference data. The goal is to reduce dependence on continuous lab access while maintaining clear, measurable confidence in the simulation results.
What you will do:
Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin
covering LTE, 5G NR.
Integrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulations
through stable and maintainable interfaces.
Model the interaction among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.
Extend the current LTE simulation capability and define reusable abstractions that support additional 5G NR and
2G stacks without duplicating the platform.
Design a fidelity ladder that combines high-fidelity PHY execution with faster calibrated models or lookup/surrogate backends, selecting the least expensive model that is valid for each engineering question.
Develop and evaluate AI/ML-based RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.
Build representative datasets and experiment pipelines; establish conventional algorithmic baselines;

We are looking for a Technical Lead to own the end-to-end architecture and technical direction of these AI systems. This is a hands-on senior individual contributor role, combining architecture, coding, technical leadership, and mentorship.
You will work from our Kfar Saba site alongside RAN, PHY, and software teams, building production-grade AI solutions for real cellular networks.
What you'll do:
Define the end-to-end architecture for AI/ML systems, including agent orchestration, model serving, data pipelines, RAG, and evaluation infrastructure.
Drive technical decisions around models, frameworks, deployment strategies, and build-vs-buy approaches.
Prototype and implement critical components while setting engineering and code-quality standards.
Lead AI solutions from prototype to production, including CI/CD, monitoring, model lifecycle, versioning, and rollback.
Establish evaluation frameworks, benchmarks, and safety criteria for AI systems operating on network data.
Mentor engineers through architecture discussions, design reviews, and code reviews.
Collaborate with RAN Systems, PHY, L2/L3, Product, and customer-facing teams to translate network challenges into practical AI/ML solutions.
Contribute to technical roadmap discussions and customer-facing architecture discussions.

We are looking for an experienced AI Engineering Manager to lead our AI team within R&D and drive the development and integration of AI capabilities across our RAN product portfolio.
The team works on production-focused AI solutions, including agentic AI for network operations, AI-driven optimization and automation, technical assistants, and automated root cause analysis. In this role, you will combine technical leadership, people management, and execution ownership to take AI initiatives from concept through productization and measurable impact.
You will define the technical direction and roadmap, lead and grow a multidisciplinary team of AI/ML, software and domain engineers, and work closely with R&D, Product, Systems and global teams to bring scalable AI capabilities into our products.
What you will do:
Lead the AI team and own its technical direction, roadmap and execution.
Drive AI/ML initiatives from concept and prototyping through production deployment and ongoing optimization.
Define clear success metrics and ensure AI solutions deliver measurable product and business impact.
Lead architecture and technical decision-making around AI models, platforms, tools and data.
Build and grow a strong multidisciplinary team, including recruitment, mentoring, performance management and career development.
Establish strong engineering practices around evaluation, scalability, reliability, monitoring and model lifecycle management.
Work closely with Product Management, Systems Engineering and R&D teams across Israel, India and the US.
Partner with RAN and domain experts to identify opportunities where AI can improve network performance, automation and operational efficiency.
Support customer-facing discussions, trials and PoCs where relevant.

The Director of AI Enablement is both a builder and a transformation leader, someone who can identify opportunities, design and implement AI solutions, and drive adoption across the organization to create measurable business value.

Reporting directly to the CEO, this is a highly visible, hands-on leadership role at the intersection of business transformation, technology, and organizational change.

The successful candidate will lead AI roadmap, build practical AI solutions, drive adoption across functions, and help transform how work is performed throughout the organization.

What Will You Do?

Define and execute AI roadmap.

Identify, prioritize, and implement high-impact AI opportunities across the business.

Partner with business, operational, engineering, and corporate functions to understand workflows, pain -points and improvements opportunities.

Translate business challenges into practical AI use cases and measurable outcomes.

Design, build, pilot, and scale AI-powered solutions, including AI agents, copilots, automations, knowledge assistants, and business applications.

Partner with IT, Engineering, Infrastructure, and Security teams to ensure scalable, secure, and sustainable solutions.

Lead the transformation, not just the technology; drive AI adoption across departments and help leaders redesign processes and ways of working to maximize AI value.

Work hand-in-hand with HR and business leaders to lead the organizational transformation required to successfully adopt AI, build internal capabilities, and embed new ways of working across the company.

Establish and lead the core pillars of the AI Enablement program: Adoption, Infrastructure, and Solution Development.

Build and lead a community of AI Builders and Leaders across the organization.

Establish AI governance, Responsible AI practices, and security standards.

Measure and communicate business impact through adoption, productivity gains, efficiency improvements, time savings, and ROI.

Success is measured not only by building AI solutions, but by driving meaningful improvements in productivity, efficiency, quality, decision-making, and business performance across the organization.

Were looking for a driven PHY SW Engineer to join our R&D team and help develop advanced, carrier-grade 4G cellular communication solutions that push performance, reliability, and AI driven development forward.
What you will do:
Design and build high-performance digital processing software for the baseband modem.
Optimize, debug, and root-cause complex real-time issues in carrier-grade systems.
Integrate, test, and bring up software modules all the way into the product.
Help shape software architecture for advanced cellular PHY solutions.
Apply agentic software development and LLM-based workflows.

We are looking for Automation QA Engineer to join our team. In this role, you will be the quality owner for the Orion system - ensuring every release meets the strictest performance, reliability, and safety requirements expected from a tactical‑grade navigation solution. You will work closely with algorithm developers, embedded engineers, system integrators, and field teams.
Responsibilities:
Design, develop, and maintain automated test suites for the Orion system.
Build automation flows validating logic, algorithm performance, and sensor integration.
Run tests on simulated environments and physical devices.
Execute manual and automated tests across stress, performance, and environmental conditions.
Analyze logs, debug issues, and verify fixes.
Define and maintain QA procedures, regression plans, and verification matrices.
Contribute to CI/CD infrastructure and automation.
Collaborate with system integrators and field teams.
Collaborate with production team and develop automation testing flows also for product delivery testing (ATP).
Writing test documentation (STP / STD / STR) based on SRS / SRD
Designing and executing functional and system-level test cases
Performing System and Integration testing for software-hardware integrated systems

We are looking for a Windows platform expert with deep, hands-on scripting skills (PowerShell preferred) to own the security hardening, imaging, and deployment automation of the Windows environment underlying our FFRangio system.
You'll work at the intersection of low-level Windows internals (boot security, disk encryption, image management) and modern Windows virtualization and DevOps practices (CI/CD pipelines), helping us build a secure, locked down, and reliably reproducible Windows environment for a regulated medical device product.
You should be comfortable going deep - from BIOS settings and TPM chains up through image-building and pipeline automation - and who treats scripting as the primary tool for making security and deployment repeatable, auditable, and reliable, in service of a real clinical product used in the Cath lab.
Responsibilities may include the following and other duties may be assigned:
Design, implement, and maintain PowerShell-based automation for system configuration, hardening, and deployment of FFRangio Windows workstations
Define and enforce BIOS/UEFI security settings and TPM-based trust chains (Secure Boot, measured boot, TPM attestation)
Implement and manage BitLocker and other Windows encryption mechanisms, including key management and recovery strategies
Build and maintain Windows hardening baselines using Group Policy, AppLocker, custom Shell/UI restrictions, and Kiosk (Assigned Access) mode suited to a clinical, cath-lab environment
Manage user/account permission models and least-privilege access schemes across managed devices
Create, manipulate, and maintain VHD/VHDX-based images for deployment, recovery, and testing workflows
Build and maintain CI/CD pipelines in Azure DevOps for automated build, test, and deployment of Windows images and configurations
Collaborate with software, QA, and regulatory/quality teams to ensure hardening and deployment practices
Document configurations, scripts, and processes for reproducibility and audit purposes, consistent with medical device quality system requirements.