AI Talent War: Average Annual Salary of 400,000, Big Tech Companies as a Plus
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An annual salary of 300,000 is considered 'entry-level'?
Recently, high-paying job postings for technical talent by domestic and international companies have pushed the employment boom in the AI field to new heights.
On September 21, a report from Indeed, the world's largest job search website, showed a sharp rise in job postings and salary levels related to Gen AI.
As of the time of writing, salary data for AI positions on Indeed's website...
At the end of August, Zhou Hongyi, founder of 360 Group, posted on his personal social media account that 360 Brain is continuously recruiting R&D talent. On recruitment platforms, 360 has 194 open positions, with AI-related roles offering monthly salaries ranging from 30,000 to 60,000 yuan.
On July 28, streaming platform Netflix posted a job listing on its website for a high-paying AI Product Manager for its Machine Learning Platform, with an annual salary range of $300,000 to $900,000 (approximately 2.2 million to 6.6 million yuan).
In June this year, salary website Levels.fyi compiled reports indicating that OpenAI engineers earn up to $925,000 annually (approximately 6.8 million yuan).
Currently, the most in-demand positions in the AI industry are concentrated in roles such as Algorithm Engineers, AI Specialists, and Prompt Engineers. According to the "2023 AIGC Employment Trends Report" released in July, the average annual salary for AIGC job postings in the past year was 401,200 yuan, 80,900 yuan higher than the average AI job salary during the same period.
With such high salaries, what kind of talent is most needed in the AI industry? What are the requirements? Which job seekers are more likely to stand out? "Top AI Player" spoke with some AI professionals and investors to provide insights for those looking to enter the field.
01. Algorithms and Data Dominate AIGC Job Postings
Data shows that since 2023, China has added 380,000 AI-related enterprises, with 135,000 new companies registered in just three months. (Note: Data as of the end of September, counting only companies with names, business scopes, or product names containing the keywords "artificial intelligence" or "AI.")
AI has become a hot industry at the forefront of innovation, with companies rapidly increasing their demand for AI-related talent. Not only are tech giants like Baidu, Tencent, Alibaba, and ByteDance offering hefty salaries to "snatch" talent, startups and small-to-medium enterprises are also recruiting tech professionals.
"Data," "computing power," and "algorithms" are the three indispensable elements of AI. Similarly, in the AIGC field, job postings show strong demand for Algorithm Engineers, Natural Language Processing specialists, and other R&D talent.
The "2023 Pan-Internet Industry Talent Mobility Report" indicates that in the first half of this year, the most in-demand roles in the pan-internet industry were concentrated in AI, with Algorithm Researchers topping the list of talent shortages.
On a certain recruitment website, searching for "artificial intelligence" in Shanghai yields only 18 job postings requiring less than one year of experience, while over 500 postings require three or more years of experience.
"Top AI Player" roughly estimates that core technical R&D roles like Algorithm Engineers, AI R&D Engineers, and Data Engineers account for more than half of the demand. Most positions require 3-5 years of experience, though some explicitly state "experience not required."
These positions offer monthly salaries ranging from 25,000 to over 400,000 yuan. In cities like Beijing, Shanghai, Shenzhen, and Hangzhou, fresh master's graduates without experience can expect an annual salary of around 250,000 yuan for algorithm engineer roles, while other cities maintain stable offers near 200,000 yuan. For algorithm positions requiring 3+ years of experience, compensation is mostly tied to capability, with some roles reaching millions annually.
Beyond traditional R&D roles in algorithms and front-end development, the popularity of large models has sparked demand for new AI service roles like 'prompt engineers' and 'data annotators'.
Prompt engineer positions require expertise in specialized knowledge, programming experience, and familiarity with AI products. Their main responsibilities include optimizing prompts for large model training and collaborating with business teams to enhance product functionality and user experience.
Meanwhile, the widely discussed 'data annotator' roles represent lower-barrier entry points in AIGC-related jobs. Most data annotation work involves organizing and labeling data, requiring basic computer skills with no specific degree or experience prerequisites—just attention to detail. These positions offer monthly salaries between 5,000-8,000 yuan, with some companies paying hourly rates of 10-25 yuan.
For non-technical candidates, conventional positions like product operations, marketing, design, and sales remain standard in AI companies.
'Top AI Players' also discovered that some AI product manager roles command annual salaries up to 720,000 yuan, significantly higher than comparable positions in other industries. This likely reflects the urgent need for professionals who understand both AI technology and product development in this field.
02. AI Talent Competition: High Salaries Come with High Requirements
The attractive compensation in AI has drawn many job seekers. However, securing top salaries requires not only strong technical skills but also practical AI experience.
Yan Changsheng, a senior engineer specializing in deep learning framework development, explains that AI algorithm engineers specifically focus on deep learning algorithms, using statistical methods to solve problems with an engineering orientation.
Most technical roles demand candidates capable of independent algorithm design and model construction, with front-end development experience, proficiency in Python and other programming languages, familiarity with machine/deep learning algorithms, and experience in fields like image/audio/NLP processing.
Li Yitong, an investor in early-stage AIGC startups, notes that the industry's most acute shortage is specialized technical talent requiring elite academic backgrounds—typically master's or PhDs from top universities with research experience in renowned labs and industrial applications.
For example, a healthcare prompt engineer position requires designing/optimizing medical AI model prompts to ensure high-quality outputs for specific scenarios, plus collaboration with medical teams. Preferred qualifications include familiarity with clinical workflows or strong medical knowledge. Such roles currently offer 20k-40k monthly salaries (14 months' pay annually).
Data-related positions focus on 'building AI data support systems' and require robust analytical capabilities. One AI startup's internship listing seeks candidates proficient in mainstream deep learning frameworks (TensorFlow, PyTorch, etc.) who can implement new models using existing APIs.
Some AI R&D engineer positions focus more on exploring and innovating key technologies for large models, requiring backgrounds in computer science, communications, or related fields, with a preference for candidates holding a master's degree or higher and having published papers in conferences like CVPR, ICCV, ECCV (Computer Vision) or NIPS, ICML, AAAI (Machine Learning).
Zheng Chuanjun, CEO of an AI image recognition and algorithm research company, told "Top AI Player": "Since our focus is on the SAAS model, our team is still small, with fewer than 10 members in the AI R&D team, but most are mid-to-senior-level engineers. Besides the AI and metaverse R&D teams, we also have some JAVA and front-end technicians."
According to "Top AI Player," most AI startups are technology-driven companies. For example, JINA AI, a company specializing in multimodal AI, has over 80% of its staff as technical personnel. In such cases, even conventional roles like "operations" require candidates to have a technical background or a strong curiosity about cutting-edge technologies.
Zhang Sa, who works in technical operations at JINA AI, said: "At the very least, you need to know how to install PyTorch (a deep learning platform), GPU drivers, adjust model parameters, and run Docker before you can even discuss problems."
Most hiring companies hold the view that candidates must align with their R&D direction and preferably have relevant technical or project experience. The industry is highly competitive, so they need people who can start contributing immediately.
Among the popular roles, non-technical positions like AI product managers and operations also require certain AI tool skills. These roles no longer focus on 'how to create AI with technology' but rather on 'whether AI can be used to enhance productivity.' One company hiring an "AI Trainer" explicitly stated that candidates should be AI enthusiasts with in-depth knowledge of trending tools like Midjourney and Stable Diffusion.
Many roles labeled as "AIGC" (AI-generated content) in content creation and design are similar to traditional jobs but come with additional AI skill requirements and higher salaries.
03. Who Can Lead the AI Job Market?
In the AI boom, what kind of job seekers are more likely to succeed?
On mainstream domestic job platforms, some AI job postings don’t seem very different from traditional internet tech roles. Some even list "experience in internet companies" as a plus.
A recruiter mentioned that candidates with experience in large tech companies can often apply algorithms to real-world business scenarios faster, quickly delivering product and commercial value.
The rich business scenarios and user data in internet companies provide strong support for the design, iteration, and implementation of AI algorithms. The AI field values internet work experience, and the internet industry is also the most eager for AI talent.
However, some job seekers transitioning from internet giants to AI face widespread anxiety. Although the AI field urgently needs senior technologists and architects, non-AI professionals find it harder than expected to pivot into AI roles.
On one hand, the rapid pace of AI research makes it difficult to keep up, raising the barrier to entry. On the other hand, understanding and applying large model technologies often requires computational resources and real-world business scenarios, making it hard to quickly translate theoretical knowledge into practice.
A job seeker nicknamed 'Rooters,' who interviewed at 24 major AI companies in China, shared on social media that the large model field is intensely competitive. During interviews, they encountered many new models, with research papers updating faster than they could read—'the output outpaces my reading speed.'
However, Li Yitong told 'Top AI Players': 'Many professionals in the internet industry have opportunities to transition directly into AI. Technical personnel are usually the first to ride the AI wave and can more easily seize the benefits of the technological boom.'
An AI startup shared its organizational structure with 'Top AI Players': The founder has experience as a product manager, management consultant, and VC investor, while the team includes full-stack engineers from major tech companies like BAT. They are currently focusing on developing applications with 'GPT chat interaction + Text to Link' logic.
Candidates who understand underlying technical principles and can apply AI to real-world scenarios to solve specific problems stand out in the AI field.
If technical skills are essential, do non-technical professionals still have a chance in this AI wave? For emerging companies, criteria for hiring include familiarity with AI tools, understanding of AI products, and insights into trending AI applications—regardless of technical background.
Li Yitong believes that beyond foundational technology, applying AI to real-world scenarios requires support from product, operations, sales, and other roles. 'The key is finding your niche.'
AI entrepreneur Bain, who works on AI technology and solutions, stated: 'The AI field is currently the most promising and hottest sector, with the highest potential for investment and tangible results.' After the rise of large models, many professionals from fields like security, law, copywriting, and emotional analysis are exploring intersections with AI. 'Everyone can try their hand in AI.'
As AIGC applications proliferate, the field becomes more accessible. With a few days of learning AI basics, even non-experts can engage in AI-related work. Low-code and no-code environments allow non-technical users to build and deploy applications without programming. Tools like TensorFlow and PyTorch have also evolved to be more user-friendly.
Zhang Sa noted that entering the AI field two years ago required deep technical knowledge, but today, prompt engineering has made AI development more intuitive. 'New technologies and interactions level the playing field, allowing newcomers to compete with industry veterans.'
As an emerging field, AI creates new gaps and opportunities. Regardless of background, continuous exploration and learning can open doors in AI. Finally, here’s advice from AI professionals and investors:
After every tech boom, only a few startups survive and thrive. The AI industry’s rapid innovation brings unexpected challenges, and most companies operate with lean, high-skilled teams. While entering the field can be tough, those who successfully turn 'generative AI' into 'productive AI' will go far.