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We are looking for a hands-on systems engineer to join the development of a multi-RAT digital twin for our Open RAN solution. The role will involve contributing to the integration of production RAN software with PHY, channel, UE, traffic, and network models, and supporting the development of simulation capabilities for LTE, 5G NR, and 2G. This is a great opportunity to gain hands-on experience in RAN systems, simulation, and AI/ML within a highly technical environment.
What will you do:
Contribute to the development of a modular, multi-RAT RAN digital twin covering LTE and 5G NR.
Develop and integrate production software components with simulation and analysis tooling using stable and maintainable interfaces.
Work with software and system teams to model communication-system behavior, including traffic, mobility, scheduling, interference, and performance KPIs.
Support the extension of existing LTE simulation capabilities and reusable components for additional radio technologies.
Develop software components and tools in C/C++ and Python for simulation, automation, data processing, and analysis.
Support the development and evaluation of AI/ML-based capabilities for communication systems and network optimization.
Build datasets, experiment pipelines, and automated analysis tools to evaluate system behavior and algorithm performance.
Create reproducible tests and experiments across software builds and algorithm versions using defined scenarios, configurations, and KPIs.
Support simulation verification and validation activities, including comparison with lab results and analysis of model behavior.
Build automated unit, component, end-to-end, regression, and performance tests and integrate them into CI/CD workflows.
Improve simulation speed, usability, observability, and automation so engineering teams can run repeatable experiments independently.
Debug issues across C/C++, Python, MATLAB, system software, configuration, and reference measurements.
Document assumptions, supported scenarios, limitations, and experiment results in a clear and structured way.
Work closely with RAN, PHY, system, AI/ML, automation, and validation teams to turn engineering questions into measurable experiments.

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.