AI Model Engineer

Req #

WD

Career Area:

Artificial Intelligence

Country/Region:

United States of America

State:

North Carolina

City:

Morrisville

Date:

Wednesday, December 10, 2025

Working Time:

Full-time

Additional Locations:

• United States of America - North Carolina - Morrisville

We are Lenovo. We do what we say. We own what we do. We WOW our customers.

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. Lenovo's continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

This transformation together with Lenovo's world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit , and read about the latest news via our StoryHub.

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 controllability.

• 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 a related technical field.

• Strong programming skills in Python.

• Deep familiarity with PyTorch, TensorFlow, 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 (instruction tuning, tool-use tuning, chat models).

• Experience with RLHF or preference-based learning (e.g., reward models, DPO, PPO-style training).

• Familiarity with distributed training frameworks (e.g., DeepSpeed, FSDP, Megatron, XLA, custom sharding strategies).

• Prior work on safety, bias, robustness, or alignment of large models.

• Publications, open-source contributions, or strong public projects in large-scale ML.

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.

Additional Locations:

• United States of America - North Carolina - Morrisville