When job hunting in North America, many international students are now applying for roles related to large language models (LLMs), including LLM research, applied engineering, and data processing. These positions typically require solid engineering skills and a good understanding of today’s AI ecosystem. A good resume should demonstrate both technical depth and the practical value of your projects. Compared to clichés and keyword stuffing, employers care more about what you’ve actually done, what problems you solved, what methods you used, and whether the work is applicable in real scenarios.
It’s recommended to keep your resume to one page, with a clear format and well-organized sections. At the beginning, the Summary section can briefly introduce your background — for example, a master’s student, major focus, familiar frameworks, and platforms. There’s no need to include too much aspirational language. The core part should be your project experience. For LLM-related roles, the most common projects include model fine-tuning, inference optimization, training data processing, and building RAG systems. For instance, if you use LoRA or QLoRA to fine-tune LLaMA-2 or Mistral, you should mention the dataset size, training methods, toolchain, and performance improvements. Avoid simply writing “fine-tuned model.” Instead, write something like: “Applied QLoRA to fine-tune LLaMA-2-7B on domain-specific data (200k samples), achieving a 15% BLEU improvement with 40% lower memory usage.”
Some students have worked on RAG systems, embedding retrieval, or agent systems — you should also clearly state which frameworks you used, such as FAISS, LangChain, Weaviate, etc., and whether you dealt with embedding caching, sharded deployment, document cleaning, etc. These details reflect whether you truly understand the full pipeline and showcase your engineering capability.
In the Skills section, it’s recommended to categorize your skills — for example:
- Modeling: Transformers, LoRA, PEFT
- Frameworks: PyTorch, Hugging Face, DeepSpeed
- Infra: Docker, Kubernetes, AWS, GCP
This makes it easier for HR to quickly understand your skill structure. Additionally, if you’ve worked on open-source projects, have a GitHub link, or have actual deployment experience, you can appropriately include this in your project descriptions — but don’t overload your resume with irrelevant links.
For international students, it’s important to use clear and concise verbs in your wording to avoid “Chinglish” expressions. The focus should not be on exaggerated descriptions, but on expressing what you did in the team or project truthfully and accurately.
Although LLM-related roles have high technical requirements, there are also many opportunities. As long as you present your content properly, your resume can be the first key step in landing an interview.
Release time:2025-05-27
