Top AI & Machine Learning Jobs

Design and deliver production-grade generative AI applications, AI agents, enterprise copilots, and intelligent automation for telecom OSS/BSS. Build Python-based AI services using LLMs, RAG, Graph RAG, knowledge graphs, vector databases, and cloud AI platforms. Train, fine-tune, optimize, and evaluate language models while developing scalable, cloud-native deployments with APIs, containers, Kubernetes, and MLOps. Collaborate with technical and business stakeholders to deliver enterprise AI solutions.

Conduct AI and machine learning research for next-generation wireless communication systems. Develop algorithms, build and evaluate models, run experiments, publish findings, and collaborate with research, development, and product teams to transition research into real-world technologies. The role also involves exploring emerging AI techniques, shaping technical direction, and contributing to Ericsson’s wireless technology strategy.

Develop data pipelines and machine learning models for 5G network challenges. Research, implement, test, and optimize machine learning algorithms and pipelines, including model training and retraining. Collaborate with data scientists, engineers, and cross-functional teams to produce maintainable, scalable code, ensure quality through unit and integration testing, and support continuous analysis and requirements management.

Conduct research and build an extensible human-in-the-loop AI agent harness. Explore clarification mechanisms, confidence indicators, advisor components, review checkpoints, and shared human-agent interaction vocabularies. Prototype agent functionality, formulate research questions, and evaluate whether richer interactions improve alignment, reduce rework, and preserve human understanding and control. The work combines AI, software engineering, interaction design, human factors, and structured user evaluation.

Defines and steers AI strategy and target architecture for autonomous telecommunications network management. Develops multi-year roadmaps toward TM Forum Autonomous Network Levels 4 and 5, establishes reusable AI patterns, and aligns product, architecture, and development teams. Guides closed-loop control, self-healing, predictive optimization, governance, explainability, observability, guardrails, and operational readiness across RAN, Transport, Core, and Cloud domains.

Master’s thesis investigating AI and machine learning applications for large-scale RF production test data at Ericsson. The work includes feature engineering, anomaly detection, root cause analysis, quality-risk prediction, test coverage optimization, visualization, explainable AI, and proof-of-concept development. The candidate will analyze manufacturing and RF performance data to generate engineering insights, improve production quality, and reduce test time.

Research whether generative AI can author, troubleshoot, and verify network device configuration templates. Responsibilities include building an automated staged judge for format, translation, compilation, simulation, and command correctness; developing AI pipelines for template generation and repair; comparing outputs with expert templates; evaluating feedback-driven retries; diagnosing failures; and assessing whether simulation success predicts real-device performance.

Conduct research on energy-efficient AI methods for detecting anomalies in telecommunications logs. Responsibilities include reviewing literature, testing machine-learning algorithms, implementing techniques, developing an end-to-end prototype, and evaluating detection performance, computational requirements, and energy efficiency. The student will analyze results, identify improvements, and work independently while collaborating with Ericsson’s team.

Investigate video quality and Quality of Experience in interactive services such as cloud gaming and remotely controlled vehicles. The thesis involves evaluating AI-based video understanding models, developing video analysis tools and quality metrics, identifying key quality factors, conducting subjective user tests, and analyzing results. Candidates will apply research methodology, algorithm development, experimentation, computer vision, video streaming, media coding, mathematical analysis, and Python programming.

Master’s thesis investigating energy-efficient AI and machine-learning methods for predicting battery state of charge in radio base stations. Responsibilities include literature review, algorithm selection and implementation, end-to-end prototype development, model evaluation using battery and operational data, and analysis of accuracy, computational requirements, energy efficiency, and limitations. The project also explores opportunities to improve battery management and energy utilization in telecommunications power systems.