Top AI & Machine Learning Jobs

Investigate how generative AI and agentic approaches can improve software test chains and quality assurance. The thesis may involve evaluating current tools and workflows, developing or improving testing automation, designing an optimized testing scenario, conducting an in-depth study of a testing phase or tool, and assessing the return on investment of proposed changes.

Design and deploy AI/ML capabilities for Ericsson products, including intelligent automation, operational analytics, generative AI, knowledge management, and developer-assistance solutions. Lead innovation from research through production, influence AI/ML architecture and product strategy, and collaborate with product and engineering teams to deliver scalable, cloud-native systems.

Support Ericsson’s Network and Managed Services Sourcing organization by identifying high-value AI use cases, analyzing data and workflows, prototyping generative AI solutions and agentic workflows, and enabling responsible adoption across sourcing teams. The role involves stakeholder collaboration, process improvement, management reporting, knowledge sharing, and developing scalable AI playbooks during an 18-month early career rotation program.

Support the design, development, and implementation of AI models and algorithms with Ericsson’s Beijing Radio R&D team. The intern will apply machine learning concepts to telecommunications projects, use programming and AI frameworks, solve analytical problems, collaborate with team members, and gain hands-on experience with real-world AI applications.

Designs enterprise AI, machine learning, and generative AI architectures for scalable SaaS solutions. Translates stakeholder needs into implementation roadmaps, selects technologies, guides technical reviews and governance, and ensures security, privacy, compliance, reliability, and cost efficiency. Partners with architects, data scientists, MLOps, DevOps, and business teams; mentors engineers; supports agile delivery, proposal development, and go-to-market activities. The role requires hands-on cloud AI deployment, LLM, MLOps, microservices, data engineering, and Python expertise.

Lead the design, deployment, and production scaling of machine learning, statistical, LLM, and agentic AI solutions. Build RAG applications, multi-agent systems, model evaluation frameworks, and reusable AI tools. Partner with engineering and product teams to identify opportunities, prepare datasets, improve model reliability and fairness, and guide initiatives from problem definition through continuous improvement.

Master’s thesis investigating machine learning model aggregation across cell, frequency, sector, node, and network levels in radio access networks. The work involves comparing coverage models, evaluating batch prediction performance, studying model retraining lifecycle strategies, and presenting results to Ericsson’s development team using real network measurement data.

Develop an AI-based Semantic Assurance Agent for autonomous networks. The thesis will investigate semantic equivalence chains to verify that AI agent transformations preserve original guardrails and constraints, identify acceptable deviations, and detect constraint breaches. Responsibilities include designing the experimental setup and comparing the agent’s results against a baseline.

Conduct a master’s thesis comparing neural network and decision tree models for radio network coverage prediction. Train and evaluate models using real Ericsson radio access network data, comparing prediction accuracy, memory usage, and CPU efficiency. Present the findings to Ericsson’s development team.

Develops statistical, machine learning, and data analytics solutions for supply chain and spare parts management. Responsibilities include collecting and preprocessing data, building predictive and prescriptive models, anomaly detection, recommendation engines, visualizing insights, supporting data-driven decision-making, communicating findings to stakeholders, exploring AI developments, and mentoring colleagues. Requires supply chain knowledge, strong analytical and statistical skills, Python and SQL experience, hands-on machine learning implementation, and cloud-based ML experience.