Staff AI Engineer, LLM Researcher

Staff AI Engineer, LLM Researcher

Lenovo

Farnborough, United Kingdom

Responsibilities

• Define research agenda: Identify high-impact research problems aligned with product needs. Set technical direction for intent understanding and agentic learning capabilities. Translate BU requirements into research roadmaps.
• Architect learning systems: Design end-to-end intent classification and agentic learning architectures. Make key decisions on model selection, training strategies, and evaluation frameworks.
• Lead RLHF & alignment research: Own the design of reinforcement learning pipelines for agent optimization. Define reward modeling approaches, safety constraints, and alignment strategies.
• Drive research-to-production pipeline: Ensure research outputs meet production quality standards. Partner with Agentic Engineers on model integration, latency optimization, and deployment.
• External research engagement: Author internal whitepapers and (where appropriate) external publications. Represent Lenovo at conferences, workshops, and industry events.
• Mentor and grow researchers: Guide junior researchers on problem formulation, experiment design, and paper writing. Create an environment of technical excellence and continuous learning.
• Cross-functional leadership: Coordinate with Infrastructure team on GPU clusters and MLOps. Work with Data team on data requirements. Support BU teams in translating research to product features.

Core Skills

• Strong foundation in deep learning: PyTorch, transformer architectures, attention mechanisms, training dynamics.
• Hands-on experience with HuggingFace Transformers, tokenization, and embedding models.
• Expert level knowledge of parameter-efficient fine-tuning methods (LoRA, adapters) and PEFT libraries.
• Understanding of classification metrics (precision, recall, F1) and experiment design principles.
• Proficiency in Python, with experience in data processing (pandas, numpy) and visualization (matplotlib, seaborn).
• Ability to read and implement techniques from academic papers.

Bonus Skills

• Experience with reinforcement learning (PPO, DPO) or RLHF pipelines (TRL library).
• Familiarity with distributed training (DDP, FSDP, DeepSpeed).
• Background in NLP tasks: NER, semantic similarity, question answering, or dialogue systems.
• Experience with experiment tracking tools (MLFlow, Weights & Biases).
• Exposure to agentic AI concepts (ReAct, chain-of-thought, tool use).
• Industry experience at leading AI labs.

Qualifications

• PhD in Computer Science, Machine Learning, NLP, or related field; MS with exceptional publication record considered.
• 5+ years post-PhD (or 7+ years post-MS) experience in ML research, including industry experience.
• First-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, EMNLP) with demonstrated citation impact.
• Track record of research translated to production systems or products.
• Experience mentoring junior researchers or leading small research teams.