Senior AI Researcher

Company description:Ericsson ABJob description:   Join our Team   About the opportunity Join and define the future of connectivity at Ericsson Research. AI is central to Ericsson’s 6G vision for intelligent, sustainable, and secure networks. You’ll lead research where machine learning, advanced reasoning, live network data, and real telecom systems meet, shaping technologies and standards with global impact. The team works across research horizons: collaborating on current product opportunities, exploring emerging AI/ML through proofs of concept, and investigating future telecom technologies. Its focus is AI for radio access networks, spanning radio layers one to three. You’ll work with global colleagues and experts across radio, networking, compute, academia, industry, and standards. Research teams develop AI/ML algorithms and collaborate with product teams to advance promising results toward deployment. What you will do •    Set research direction and lead initiatives in AI/ML for future radio access networks and AI-native systems. •    Identify and develop AI approaches for radio access network functions. Benchmark results against existing methods and real data. •    Architect algorithms, models, and simulation frameworks. •    Drive research from concept to prototypes, publications, patents, and standards contributions. Work with product teams to transfer promising algorithms toward real-world use. •    Lead and mentor researchers, translate objectives into actionable research plans, and present findings to senior leaders and external partners. •    Represent Ericsson Research in collaborations across academia, industry, and standardization bodies. Skills you bring •    A PhD in AI, data science, computer science, engineering physics, robotics, or a closely related field. •    Typically 3+ years of relevant research experience beyond your PhD, with demonstrated technical leadership and a track record of publications, patents, or standards contributions. •    Deep knowledge of modern AI/ML, such as deep learning, reinforcement or distributed learning, generative AI, probabilistic modelling, stochastic optimization, or graph-based methods. •    Strong programming skills in Python, Java, or C++, and experience with PyTorch, JAX, TensorFlow, or similar frameworks. •    Hands-on experience designing, training, and validating ML systems at scale. You can explain practical choices around model architecture, input data, data quality, and implementation. •    Experience with simulation-based evaluation. •    Technical leadership, mentoring, and clear English communication skills. You can turn high-level goals into team plans and explain complex AI concepts to non-specialists. •    Experience in telecom, networking, robotics, or signal processing is valuable. Strong AI expertise is essential; radio domain knowledge can be developed in collaboration with experienced specialists.