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We are looking for a highly motivated and passionate Sr. AI PHY System Engineer to design, implement, and integrate advanced cellular communication systems for our next-generation base-station products. The role leads PHY activities from feature development through integration, with a strong emphasis on automation and the use of modern AI tools to accelerate development, debugging, and validation.
What you'll do:
Leverage existing AI tools (AI coding assistants, LLM-based workflows) to speed up development, code comprehension, debugging, and documentation.
Provide technical leadership and mentorship to junior engineers within the PHY team, including sharing best practices for automation and AI-assisted development.
Act as the focal point for all PHY and RF-related aspects.
Define and write system-level requirements.
Continuously optimize and fine-tune PHY-layer algorithms to enhance the performance and efficiency of base-station products.
Build automation and internal tooling to streamline PHY development, integration, and lab testing - reducing manual effort and improving repeatability.
Work within existing codebases and repositories: understand the current architecture quickly, extend it responsibly, and add new capabilities without destabilizing established flows.
Design and develop new tools (test harnesses, analysis utilities, reporting dashboards) to support PHY bring-up and validation.
Collaborate closely with cross-functional teams to ensure seamless integration of PHY components with software, digital signal processing (DSP), and other subsystems.
Hands-on lab work.

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 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 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 highly motivated, experienced, and passionate wireless algorithm experts for the research and design of advanced cellular communication algorithms, leveraging neural networks and machine learning techniques, for our 5G and beyond products.
What you'll do:
Conduct algorithmic research, balancing performance, implementation cost, real-time constraints, and time-to-market, with a strong focus on ML-based approaches for PHY layer processing.
Design and train neural network models for PHY tasks such as channel estimation, signal detection, beamforming, and decoding, targeting real-time inference on embedded platforms.
Develop algorithms from initial research and simulation through to official customer releases, including literature reviews, ML model prototyping using Python, PyTorch, and TensorFlow, MATLAB modeling, specification writing, and support throughout implementation and end-to-end integration.
Evaluate and benchmark ML-based solutions against traditional DSP approaches, considering accuracy, latency, computational cost, and overall system performance.

We are looking for a AI-Driven 4G PHY SW Tech Lead to work on the architecture, implementation, and real-time optimization of the LTE Layer 1 (PHY) software stack. You will be a technical authority for the domain, working across MAC, System, Integration, QA, and Product Management to deliver carrier-grade Open RAN solutions. A core part of the role is advancing how the team applies AI and agentic software development to a large, high-performance codebase, accelerating design, debugging, and delivery.
What you will do:
Lead the design, development, and evolution of the LTE Layer 1 (PHY) software stack.
Drive PHY features end to end: architecture, implementation, optimization, testing, and production support.
Drive performance optimization, debugging, and root cause analysis for carrier-grade real-time reliability.
Shape the software architecture and technical roadmap of our Open RAN platform.
Mentor engineers, set the bar for engineering practice, and partner with MAC, System Engineering, Integration, QA, and Product Management.
Apply agentic software development and LLM-based workflows, and evaluate new tools as they emerge.

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 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're looking for a highly motivated and experienced Sr. Principal DevOps Engineer to own the software delivery pipeline for our next-generation products - from commit to release. This is a build-and-release-centric role: you'll design the CI/CD systems the entire engineering organization depends on, drive build performance and reliability, and lead the integration of modern AI tools into the development lifecycle to improve code quality and delivery speed.
What you'll do:
Own the architecture, implementation, and day-to-day reliability of our CI/CD pipelines end to end - build, test, package, publish, deploy
Cut build and test cycle times through parallelization, caching, incremental builds, and smarter agent/executor utilization
Build and maintain the build infrastructure itself: build agents, containerized build environments, artifact repositories, and dependency management
Integrate automated testing, code quality gates, static analysis, and security scanning directly into the pipeline, with clear feedback to developers
Define and enforce branching, versioning, and release strategies across teams
Treat pipelines as code - version-controlled, reviewed, reusable, and templated as shared libraries rather than copy-pasted per project
Monitor pipeline health and build reliability; hunt down flaky tests and non-deterministic builds and drive them out
Partner directly with software engineers as the internal service owner of CI - unblock failing builds, onboard new services, and make the paved path the easy path
Lead AI initiatives within the engineering group: integrate AI-driven tooling into the pipeline and the broader SDLC for code review, test generation, failure triage, and developer productivity

We are looking for a Senior Software Engineer who is comfortable operating across the entire system, from low-level Linux and networking problems to the architecture of large Go and Kubernetes-based distributed systems.
This role is for someone who can investigate a Linux kernel or networking issue in the morning, review a concurrency problem in Go before lunch, design a multi-service distributed flow in the afternoon, and help debug a C++ component when production behavior does not match expectations.
You do not need to be the deepest specialist in every technology we use. You do need the technical range, curiosity, and systems thinking to move between layers quickly and understand how they affect one another.
Our team owns RIC Systems end to end, including Near-RT RIC, Non-RT RIC, infrastructure, messaging, state management, network-facing components, microservices, deployment, performance, reliability, and production troubleshooting.
We are looking for someone who wants that breadth.
What You'll Do:
Take technical ownership across Near-RT RIC and Non-RT RIC systems, helping define architecture, reliability, scalability, and engineering direction.
Design and develop production backend services, primarily in Go, as part of a large distributed system composed of dozens of interacting services.
Move comfortably across the stack, from Linux, networking, sockets, containers, and system performance to application-level architecture and distributed system design.
Lead the design of complex multi-service flows involving messaging, distributed state, concurrency, retries, backpressure, ordering, consistency, and failure recovery.
Build and operate systems using technologies such as Kafka, NATS, Redis, Kubernetes, and comparable distributed infrastructure.
Debug difficult production issues that cross boundaries between application code, operating systems, networking, containers, infrastructure, and service architecture.