{"id":118730,"url":"https://alion.io/job/parallel-wireless-seniorprincipal-ran-digital-twin-ai-simulation-engineer","title":"Senior/Principal RAN Digital Twin & AI Simulation Engineer","company":{"id":4814,"name":"Parallel Wireless","domain":"parallelwireless.com","url":"https://alion.io/company/parallel-wireless","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":80,"open_postings":16,"ghost_share":0,"stale_share":0.75,"repost_share":0.063,"time_to_fill_p50_days":50,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kfar Saba, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":113000,"max_usd":282000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1286},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"Digital Twin","optional":false},{"name":"Git","optional":false},{"name":"Linux","optional":false},{"name":"MATLAB","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"SciPy","optional":false},{"name":"Verification & Validation","optional":false},{"name":"PyTorch","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-07-06T08:09:21Z","employer_posted_date":"2026-07-06","last_verified_at":"2026-10-30T10:32:13Z","board_verified":true,"closed_at":null,"days_open":116,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":114},"description":"Parallel Wireless is a U.S.-based pioneer in Open RAN innovation, transforming how mobile networks are built, optimized, and powered. Through our GreenRAN™ portfolio, we help operators deliver secure, energy-efficient, automated, and flexible connectivity across 2G, 3G, 4G, 5G, and the path toward 6G. Our software-centric, hardware-agnostic approach brings intelligence into the RAN while helping customers reduce complexity and total cost of ownership.\nParallel Wireless is 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.\nThis 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.\nThe 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.\nWhat you will do:\nOwn the technical architecture and roadmap for a modular, multi-RAT RAN digital twincovering LTE, 5G NR.\n\nIntegrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulationsthrough stable and maintainable interfaces.\n\nModel the interaction among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.\nExtend the current LTE simulation capability and define reusable abstractions that support additional 5G NR and2G stacks without duplicating the platform.\n\nDesign 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.\nDevelop and evaluate AI/ML-based RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.\nBuild representative datasets and experiment pipelines; establish conventional algorithmic baselines;measure accuracy, robustness, generalization, latency, and compute cost before recommending integration into production software. \n\nCreate reproducible A/B experiments across software builds and algorithm versions, using defined scenarios, seeds, configurations, and KPIs such as throughput, BLER/ACK-NACK behaviour, MCS, resource-block allocation, SINR, transmit power, latency, and fairness.\nEstablish simulation verification and validation practices: matched sim-vs-lab scenarios, calibration rules, lab-repeatability baselines, divergence analysis, model-version tracking, and evidence reports.\nPrevent overfitting the twin to a single setup by separating universal model parameters, setup-specific calibration, and the production algorithms under test.\nBuild automated unit, component, end-to-end, regression, and performance tests and integrate them into CI/CD workflows.\nImprove simulation speed, scale, observability, and usability so that stack, PHY, test, and AI engineers can run repeatable experiments independently.\nDebug discrepancies across C/C++, Python, MATLAB, PHY models, production stack behaviour, configuration,and reference measurements.\n\nDocument model assumptions, limitations, supported operating regions, calibration provenance,and the validity of every simulation or ML backend.\n\nWork closely with RAN stack, PHY, system architecture, AI/ML, automation, and lab-validation teams toconvert product questions into measurable simulation campaigns.\n\nWhat you bring:\nBSc or MSc in Electrical Engineering, Computer Engineering, Computer Science, or a related field,with substantial relevant industry experience.\n\nTypically, 7+ years of hands-on experience in communication systems, system development, integration,or simulation. Experience with RAN, modem/PHY, or wireless systems is an advantage.\n\nDemonstrated experience building or validating link-level, system-level, or hardware-in-the-loop simulationsand explaining where a model is-and is not-valid.\n\nStrong programming skills in C or C++ and Python, including the ability to integrate production native code with simulation and analysis tooling.\nPractical experience with scientific computing and data analysis using tools such as NumPy, SciPy, pandas, and visualization frameworks.\nSound experimental and statistical judgment: reproducibility, baselines, error analysis, uncertainty, calibration, controlled comparisons, and avoidance of data leakage or curve fitting.\nExperience working in Linux development environments with Git, automated testing, containers, and CI/CD. \nAbility to lead a technically ambiguous initiative, make architecture decisions, and communicateclearly across research, product, development, and validation teams.\n\nNice to have:\nA PhD in wireless communications, signal processing or a related area.\nHands-on experience developing or evaluating machine-learning models for communications,signal processing, time-series data, or related domains using PyTorch, TensorFlow, or an equivalent framework.\n\nKnowledge of LTE and/or 5G NR L1/L2 behavior, including MAC scheduling, link adaptation, HARQ,CQI/SINR feedback, resource allocation, and uplink power control.\n\nKnowledge of digital communications and signal processing, including areas such as channel estimation, equalization, coding/modulation, MIMO, propagation and fading models.\nExtend the current LTE simulation capability and define reusable abstractions that support additional5G NR and 2G stacks without duplicating the platform.\n\nExperience with MATLAB and Communications/LTE/5G toolboxes or equivalent PHY simulation environments.\nDirect experience with production eNodeB/gNodeB software, commercial modem stacks, or Open RAN products.\nKnowledge of 3GPP LTE, NR, and/or GERAN specifications and experience translating standardsinto executable models and test scenarios.\n\nExperience with scheduler algorithms such as proportional fair, round robin, maximum C/I, QoS-aware scheduling,or reinforcement-learning-based resource allocation.","description_format":"text","description_chars":6960,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Israel","iso":"IL","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial IoT (IIoT)","Mobile Networks","Network Equipment"],"lifecycle":[{"event":"open","at":"2026-07-06T08:09:21Z"}],"visa":[],"liveness":{"score":13,"band":"cold","label":"Long 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