Advisory AI Engineer - LLM Training & Optimization

CompanyLenovo

LocationEdinburgh, Scotland, United Kingdom

Posted At1/15/2026

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Occupation Type

Rail and rolling stock builders and repairers

Occupation Code Skill LevelMedium Skilled

Sponsorship EligibilityIneligible for Sponsorship

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Description

This role is open for the Edinburgh, Scotland location only. Candidates must be based there, as the position requires working from the office at least three days per week (3:2 hybrid policy).

The Lenovo AI Technology Center (LATC)Lenovos global AI Center of Excellenceis driving our transformation into an AI-first organization. We are assembling a world-class team of researchers, engineers, and innovators to position Lenovo and its customers at the forefront of the generational shift toward AI. Lenovo is one of the worlds leading computing companies, delivering products across the entire technology spectrum, spanning wearables, smartphones (Motorola), laptops (ThinkPad, Yoga), PCs, workstations, servers, and services/solutions. This unmatched breadth gives us a unique canvas for AI innovation, including the ability to rapidly deploy cutting-edge foundation models and to enable flexible, hybrid-cloud, and agentic computing across our full product portfolio. To this end, we are building the next wave of AI core technologies and platforms that leverage and evolve with the fast-moving AI ecosystem, including novel model and agentic orchestration collaboration across mobile, edge, and cloud resources. This space is evolving fast and so are we. If youre ready to shape AI at a truly global scale, with products that touch every corner of life and work, theres no better time to join us.

We are seeking a highly motivated and skilled Machine Learning Engineer to join our rapidly growing AI team. You will play a critical role in the training of large language models (LLMs), large vision models (LVMs), and large multimodal models (LMMs), including fine-tuning and reinforcement learning. This is a challenging yet rewarding opportunity to contribute to cutting-edge research and development in generative AI. Youll be working with a collaborative team to push the boundaries of whats possible with AI models and deploy them into innovative products. If you are passionate about making Smarter Technology For All, come help us realize our Hybrid AI vision!

Responsibilities

Design, implement, and evaluate training pipelines for large generative AI models, encompassing multiple stages of post-training.
Data augmentation: Design, implement, and evaluate data augmentation pipelines to increase the diversity and robustness of training datasets, improving model performance, particularly in low-data regimes.
Adversarial training: Develop and implement adversarial training techniques to improve model robustness against adversarial attacks and enhance generalization performance by exposing the model to perturbed input examples during training.

Supervised Fine-tuning (SFT): Developing and executing SFT strategies for specific tasks.

Reinforcement Learning from Human Feedback (RLHF): Running and refining RLHF pipelines to align models with human preferences.

Pruning: Design and implement model pruning strategies to reduce model size and computational complexity by removing non-essential parameters, optimizing for both performance and efficiency without significant accuracy loss.

Distillation: Develop and perform model distillation techniques to compress large language models into smaller, more efficient models while preserving key performance characteristics.

Quantization: Implement and evaluate model quantization techniques to reduce model size and accelerate inference speed, balancing precision loss with performance gains for deployment across diverse hardware platforms.

Low-Rank Adaptation (LoRA): Utilizing techniques for efficient fine-tuning of large language models, balancing performance and resource constraints, and tailoring model performance for downstream tasks well.

Experiment with various training techniques, hyperparameters, and model architectures to optimize performance and efficiency.

Develop and maintain data pipelines for processing and preparing training data.

Monitor and analyze model training progress, identify bottlenecks, and propose solutions.

Stay up-to-date with the latest advancements in large language models, training techniques, and related technologies.

Collaborate with other engineers and researchers to design, implement, and deploy AI-powered products.

Contribute to the development of internal tools and infrastructure for model training and evaluation.

Qualifications

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field and 5+ years of relevant work experience or 7+ years of relevant work experience.
Strong programming skills in Python and experience with deep learning frameworks like PyTorch.
Solid understanding of machine learning principles, including supervised learning, unsupervised learning, and reinforcement learning.
Proven experience in designing and conducting experiments, analyzing data, and drawing meaningful conclusions.
Familiarity with large language models, transformer architectures, and related concepts.

Experience with data processing tools and techniques (e.g., Pandas, NumPy).

Experience working with Linux systems and/or HPC cluster job scheduling (e.g., Slurm, PBS).

Excellent communication, collaboration, and problem-solving skills.

Bonus Points

Ph.D. in Computer Science, Machine Learning, or a related field.
Experience with distributed training frameworks (e.g., DeepSpeed, Megatron-LM).

What We Offer

Opportunities for career advancement and personal development
Access to a diverse range of training programs
Performance-based rewards that celebrate your achievements
Flexibility with a hybrid work model (3:2) that blends home and office life
Electric car salary sacrifice scheme
Life insurance

#LATC #LATC-IC

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.