Current Salary Landscape
Recently, the AI engineer position has become incredibly hot. We often see news reports about AI engineers earning million-yuan salaries or fresh graduates being snatched up by big companies with high pay. As an AI engineer with two years of experience, I want to share the real situation as I know it.
Let's talk about salary levels first. Indeed, AI engineers at major companies in first-tier Chinese cities generally earn well. Fresh graduates from top universities like Tsinghua, Peking, Fudan, or Jiaotong, or those with relevant master's degrees, typically start with annual packages around 250,000-350,000 yuan. This includes base salary, quarterly bonuses, year-end bonuses, stock options, and other forms of compensation.
One of my graduate school classmates joined a major tech company last year doing computer vision work. His compensation breaks down roughly like this: monthly base salary just over 20,000 yuan before tax, plus quarterly and year-end bonuses, bringing his annual take-home pay to around 300,000 yuan. Of course, this depends on performance reviews.
For engineers with 3-5 years of experience, reaching annual salaries of 500,000-800,000 yuan isn't difficult if they have strong technical skills. A senior colleague in our team who specialized in computer vision for 4 years recently switched companies and more than doubled his salary to over 700,000 yuan take-home pay.
As for technical experts, annual salaries over 1 million yuan are common. Especially in hot areas like large language models and multimodal AI, some experts can earn 2-3 million yuan annually. However, people at this level typically have over ten years of industry experience and deep expertise in specific domains.
Regarding salary differences, I think three aspects are crucial: technical depth, business understanding, and project experience.
Technical depth refers to your understanding of AI fundamentals. For example, you shouldn't just know how to train models with PyTorch, but understand the underlying principles and why things are done certain ways. I've seen many colleagues who can skillfully use various deep learning frameworks but don't know how to optimize when model performance or efficiency issues arise.
Business understanding is also particularly important. Many companies now emphasize AI implementation, and your value will be significantly diminished if you only understand technology without grasping business needs. I previously worked on an advertising recommendation system project, and it was deep understanding of business requirements that allowed me to improve the algorithm's effectiveness to a new level.
As for project experience, it's best to have end-to-end project experience. I know a senior colleague who previously worked on traditional computer vision tasks and switched to large language models last year. Because of her rich engineering experience, she quickly adapted to the new technology stack, and her salary increased by about 40%.
Skill Requirements
As an AI engineer, there are core skills you must master to establish yourself in this industry. I think the most fundamental are three aspects: machine learning fundamentals, programming ability, and engineering practical skills.
First, machine learning fundamentals. This is absolutely crucial and is typically tested in interviews. Our team has been hiring recently, and we always ask about basic machine learning concepts in interviews, such as how to solve overfitting and underfitting, common optimization algorithms, why regularization is needed, etc.
The Transformer architecture deserves special mention - it's basically a must-know topic now. Why? Because it's the core technology behind large language models. I suggest everyone thoroughly study the principles of the Attention mechanism, and ideally be able to implement a simple Transformer from scratch. This allows you to speak confidently in interviews.
Proficiency with deep learning frameworks is also essential. You should master either PyTorch or TensorFlow. I personally recommend PyTorch because it's more flexible in design and easier to debug. Plus, many new research works are implemented in PyTorch.
For programming skills, Python is a must. Almost all AI frameworks provide Python interfaces, and data processing and model training are done in Python. However, if you want to do core development at major companies, C++ is unavoidable.
Why learn C++? Because many performance optimizations and low-level implementations require C++. Work like model inference acceleration and CUDA programming needs solid C++ foundations. A colleague in our team missed a promotion opportunity because they didn't know C++, which prevented them from participating in many optimization tasks.
Engineering practical ability is also important. Companies now value end-to-end project experience most - the entire process from data processing through model training to deployment optimization. I've noticed many job seekers focus only on algorithms while neglecting engineering capabilities, which is a mistake.
For example, we recently did an image recognition project where the model performed well, but we encountered many problems during actual deployment. Issues like the model being too large, slow inference speed, and high memory usage all required strong engineering skills to solve. So I suggest learning about engineering practices while studying algorithms.
[Subsequent content will be sent in batches due to length limitations]
