We are seeking a highly experienced Senior Engineer to act as a technical leader and project manager within our advanced network automation and AI-native optimization teams. In this dynamic role, you will divide your time between team management (approximately 25%) and hands-on technical engineering (75%). You will lead a multidisciplinary team to deliver cutting-edge solutions for Tier-1 telecom operators, while directly contributing to core R&D tracks including advanced cellular localization, proactive AI-native link adaptation, and energy-saving initiatives. You will ensure the successful translation of theoretical models into production-ready, containerized rApps compliant with industry platforms like the Ericsson Intelligent Automation Platform (EIAP).

Team Management & Leadership (25%)

• Lead, mentor, and manage a team of software and systems engineers to ensure successful and timely delivery of project milestones.
• Act as the primary technical point of contact with external stakeholders and engineering teams, facilitating alignment and milestone reviews.
• Oversee project lifecycles spanning from ideation and requirements co-design to continuous integration, testing, and final deployment.

Technical Contribution & Track Support (75%)

Work on at least one of the following tracks:

Support the software engineering track by ensuring core algorithmic logic is efficiently packaged into containerized rApps compliant with the Ericsson Intelligent Automation Platform (EIAP).

Localization Track: Support the design and tailoring of advanced positioning algorithms utilizing cell metrics to enhance network localization accuracy.

Link Adaptation Track: Guide the development of intelligent algorithms for proactive Modulation and Coding Scheme (MCS) decisions.

Energy Track: Drive system-level simulations and algorithmic development for O-RAN energy saving use cases.

Excellent communication skills, with the ability to translate complex technical concepts into clear feasibility reports, High-Level Designs (HLD), and KPI frameworks.

B.Sc. in Communications Engineering, Computer Engineering, or related interdisciplinary fields. An M.Sc. or Ph.D. with a focus on wireless communications is highly preferred.

3+ years of industry experience in telecommunications systems engineering, 4G/5G networks, and/or applied machine learning. A proven experience in a team leadership or technical management role is highly preferred.

Some knowledge of Open RAN (O-RAN) architecture, RIC (RAN Intelligent Controller) platforms, and related 3GPP/O-RAN Alliance specifications.

Proficiency in software development ecosystems (Python, Git) and experience with network simulators (e.g., NS3, MATLAB).

We are seeking a motivated Junior System-Level Engineer to join our wireless communications research and development team. The successful candidate will design, simulate, and evaluate AI-driven cross-layer (PHY-MAC) algorithms for cellular networks, with a primary focus on link adaptation and related radio resource management (RRM) tasks.

The role sits at the intersection of wireless system design and machine learning. Specifically, a successful candidate will contribute directly to the team’s effort on the development of AI-native RAN applications for the identified use cases. The prospective engineer will formulate network optimization problems as learning problems (supervised, online, or reinforcement learning), train and benchmark models in system-level simulations, and engineer them from the start into a compliant RIC application module to facilitate deployment as xApps/rApps on an O-RAN system or a comparable open, programmable RAN platform, such as the Ericsson Intelligent Automation Platform (EIAP).

This position suits a recent graduate or early-career engineer who is equally curious about wireless fundamentals and modern AI methods. The position offers a unique learning opportunity for early-career researchers to delve into practical details and considerations in commercial mobile networks.

·   Develop and maintain system-level simulators for cellular networks, modeling PHY and MAC behavior, channels, mobility, traffic, and multi-cell interference.

·   Formulate cross-layer RAN optimization problems for the target use cases into a learning problem.

·   Apply ML/RL to related cross-layer tasks such as scheduling, power control, beam management, and resource allocation.

·   Benchmark learned solutions against conventional and classical baselines and analyze when and why learning helps.

·   Define the required interfaces, KPM measurements, and control actions, and respect near-RT (10 ms to 1 s) and non-RT timing constraints to facilitate O-RAN -like deployment.

·       Document methods, assumptions, and results through technical reports, well-structured code, and, where appropriate, research publications.

• Attend necessary online training, seminars, and keep track of state-of-the-art O-RAN global trends and advancements.
• Understand the team’s recently developed algorithms and use cases, and help in their further development taking practical considerations into account.

·       Collaborate with senior researchers on experiment design and ablation studies.

·       Collaborate with software and cloud integration teams to ensure app modules follow containerized deployment standards (e.g., Docker, Kubernetes basics).

Required

• B.Sc. in communications engineering, or interdisciplinary specializations in communications and computer engineering. M.Sc. is a plus.

·       Solid foundation in wireless communications: OFDM, MIMO, channel modeling, coding and modulation, and link-level concepts (SNR, BLER, MCS).

·       Working knowledge of PHY and MAC functions in 4G LTE and 5G NR.

·       Working knowledge of machine learning/RL fundamentals

·       Strong Python skills, with hands-on experience in PyTorch or TensorFlow and scientific libraries (NumPy, SciPy, pandas). Knowledge of other object-oriented programming languages is a plus.  

·       Experience building or using simulation tools (link-level or system-level) using tools such as NS3, MATLAB, LabVIEW, and their necessary software integration with other open-source tools whenever necessary.

Preferred (Considered a Plus)

·       Hands-on experience applying ML or RL to wireless problems (link adaptation, scheduling, power control, beam management, channel prediction), through a thesis, project, or publication.

·       Familiarity with RL toolchains 

·       Exposure to multi-agent RL, online or safe RL, offline RL, or federated and distributed learning.

·       Familiarity with O-RAN architecture 

·       Experience with one of the following simulators: ns-3, Sionna, Simu5G, OpenAirInterface, srsRAN, or MATLAB 5G Toolbox.

·       Knowledge of 3GPP specifications, and of C/C++, Docker, Git, and Linux workflows.