AI Model Engineer

* United States of America - North Carolina - Morrisville

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Lenovo is a US $69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services.

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Lenovo is seeking a highly motivated Foundation Model Engineer to contribute to the design, development, and exploration of our next-generation AI systems. As a Foundation Model Engineer, you will focus on adapting and improving foundation models for real products: pre-training, fine-tuning, post-training (RLHF/DPO), and evaluation. You’ll work closely with research and platform teams to turn large models into reliable, high-quality systems.

This is an exciting opportunity to gain hands‑on experience with cutting‑edge AI systems while collaborating with experienced engineers, researchers, and product teams to help advance Lenovo’s Hybrid AI vision and make Smarter Technology for All.

Key Responsibilities

Fine‑tune and adapt foundation models
Design and run fine‑tuning and parameter‑efficient training (e.g., LoRA, adapters, low‑rank methods) for LLMs and related models to support specific products and domains.

Implement post‑training pipelines
Build and maintain pipelines for instruction tuning, preference optimization (e.g., RLHF, DPO), and tool‑use training to improve helpfulness, safety, and cont rollability.

Data curation and labeling strategy
Work on dataset creation, filtering, deduplication, and labeling strategies; collaborate with data and annotation teams to define high‑quality training and evaluation sets.

Optimize training performance
Profile and optimize training jobs for throughput and cost efficiency across GPUs/accelerators (batching, sharding, mixed precision, memory optimization).

Model evaluation & benchmarking
Design and run evaluation suites (automatic metrics + human evals), analyze regressions, and compare model variants across internal and external benchmarks.

Production readiness
Partner with infra and product teams to move trained checkpoints into production, including versioning, rollout strategies, and monitoring model behavior in the wild.

Stay current with the field
Track advances in model architectures, optimization methods, and training techniques; propose and run experiments to bring relevant ideas into the stack.

Qualifications

2+ years of industry experience in ML, applied research, or highly relevant internships/research roles.

Master’s degree or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related technical field.

Strong programming skills in Python.

Deep familiarity with PyTorch, Tensor Flow, or JAX.

Solid understanding of deep learning fundamentals: optimization, regularization, initialization, distributed training basics.

Hands‑on experience training or fine‑tuning large models (not just calling APIs): e.g., language models, vision‑language models, or similar.

Comfort working with training pipelines, experiment tracking, and model checkpoints.

Experience building datasets for training/eval and running structured experiments.

Ability to analyze results, understand failures, and iterate quickly.

Bonus Points:

Experience with LLM‑specific training techniques…