AI Research Engineer

· Lead technical research and experimentation in User World Models, predictive AI, knowledge graphs, and data representations for Personal AI.

· Formulate hypotheses, reproduce relevant research, build baselines and measure improvements in context understanding, prediction quality, grounding and reliability.

· Develop privacy-aware representations of user activities, relationships, preferences, goals, temporal context and cross-device state; account for uncertainty, consent and changing information.

· Explore graph-based, semantic and temporal memory, entity resolution, data fusion, retrieval and reasoning across permitted application and device data.

· Investigate predictive models that anticipate user needs or next actions, with clear confidence thresholds, evaluation and user control.

· Partner with prototype and systems engineers to validate ideas in realistic Qira experiences on Lenovo devices, including proactive and cross-device scenarios.

· Contribute to patents, technical disclosures and, where appropriate, external research publications.

Basic Qualifications

· Bachelor's or Master's degree in Computer Science, AI, ML or a related technical field; PhD or equivalent research experience is a plus.

· 5+ years of relevant AI/ML experience, with strong evidence of hands-on model development or research engineering.

· Deep experience with modern LLM/SLM or multimodal AI systems and frameworks.

· Experience in one or more of user modeling, personalization, predictive modeling, knowledge graphs, information retrieval, LLMs or contextual reasoning.

· Strong Python and software engineering skills; comfortable taking ideas from experiment to working code.

· Ability to reason about model quality, latency, cost, reliability, privacy and real-world constraints.

Preferred Qualifications

· Experience with temporal/heterogeneous knowledge graphs, graph ML, entity linking, probabilistic reasoning or causal/predictive modeling.

· Experience with multimodal context, memory systems, privacy-preserving learning or cross-device data integration.

· Experience with multimodal foundation models, VLMs, speech/audio or vision-language systems.

· Publication, patent or open-source contributions in AI/ML.

Success Measures

· Deliver reproducible research experiments and working prototypes for User World Models, Predictive AI and Knowledge Graph/Data priorities.

· Demonstrate measurable improvements in contextual understanding, prediction usefulness, grounding, reliability or privacy-aware personalization.

· Demonstrate a clear path from promising experiments into advanced development or product adoption.

Key Partnerships

Works closely with the Lead Scientist/Research leadership, AI Systems, Prototype Engineering and global Qira Product/Engineering teams.