Daily salary up to 5,000 yuan! Quantitative giants set new record for high-paying "internships"

A daily wage of 1,000 yuan, taking home 10,000 yuan after a two-week internship—this seems to be the biggest imagination of “sky-high internship” pay from the outside.

But in reality, the cash-rich first-tier quantitative institutions have already broken this salary record.

According to information from the quantitative finance circle, currently, there are institutions offering “top salaries” of 3,000 to 5,000 yuan per day for the “most competent minds” among graduating students. Of course, the premise is that they must meet a series of standards for talent set by these institutions.

As AI becomes the mainstream narrative, quantitative firms continue to offer higher salaries to top human talents. On one hand, this shows that there is still potential in factor mining for quantitative investments; on the other hand, it also confirms that the most effective investment methods rely on artificial intelligence.

But this evolution from “generally high salaries” to “selective recruitment” reflects the further “ambition” of the quantitative industry in the deep waters of the AI arms race.

“Top Internship Salaries” Continue to Rise

Outside the realm of social media attention, the salaries of “top interns” have quietly continued to rise.

Behind this is the growing need for good seedlings by quantitative institutions, as well as a continuously increasing scale of assets under management.

According to third-party statistics, despite consciously restricting scale growth, currently there are more than a dozen quantitative institutions with assets under management exceeding 40 billion yuan, and several already over 100 billion yuan.

For these institutions, offering 1,000, 3,000, or even 5,000 yuan per day is well within their capacity; the core is to have the era’s most powerful “brains” serve them.

Which students are the most sought after?

According to insiders, students from Beijing’s famous “Yao Class.” The class, also known as the “Computer Science Experimental Class,” was founded by Turing Award-winning Academician Yao Qizhi. Each year, about 30 students are selected from nearly 4,000 new Tsinghua University students. Most selected are national Olympiad gold medalists or top scorers from various provinces—reputed as “the best among Tsinghua.”

According to insider information, some quantitative institutions are often willing to reserve their highest internship salaries for “experimental class” students from famous universities, with Yao Class being the most popular.

Not a Universal Salary Increase

It is worth noting that while salaries for top students are rising, the overall internship wage in the industry hasn’t seen a universal increase.

In other words, the current quantitative circle prefers highly targeted premium payments for “the best and brightest.”

When top institutions are willing to pay premiums for a very small number of candidates meeting specific standards, most job seekers outside the threshold receive offers no different from the industry average. This dual-track salary structure continues to widen the gap between top talent and ordinary job seekers.

From industry evolution, the overall median of quantitative internship salaries has moved upward in recent years, but there have been fluctuations—especially in 2024, when quantitative performance was lackluster during a bear-to-bull transition.

But as performance and scale continue to rise, the pace of talent recruitment among leading quantitative institutions is accelerating—some have even moved their core talent lock-in window to junior or sophomore year.

Why Are Top Students Needed?

Quantitative institutions are willing to pay new graduates far above the market average salary for one core reason: the core driving force for quantitative strategy returns is still people.

Essentially, the core principle of quantitative investment is systematic mining of market patterns. Two key points here: market patterns, and systematic mining. Both require particularly “brilliant” minds to innovate and upgrade.

In the practice of quantitative investing, institutions have found that whether it is mining factors, building portfolios, optimizing iterations, or even upgrading strategies, every link highly relies on mathematical intuition and abstract reasoning ability. These abilities are difficult to acquire through short-term training and are often differentiated during academic stages.

To some extent, at the forefront of quantitative research and development, a technological R&D scene is being played out. A group of top graduates with competition backgrounds and cross-disciplinary expertise in mathematics and computer science, based on the latest technological achievements and equipment, attempt to discover potential investment possibilities from vast amounts of data.

This naturally steep learning curve and leap-like strategy output curve both confirm one point:

Genius is needed here.

The Competition Continues to Upgrade

The difficulty of the work and the enormous returns once a breakthrough is achieved have turned talent recruitment among top-tier quantitative institutions into a silent arms race.

With limited strategy capacity and market space, leading institutions know that the real variable that widens competitive differences is talent density, not asset scale. Thus, locking in the best candidates ahead of competitors has become a priority for some institutions.

This competition is particularly evident in the recruitment process. It is reported that some institutions have established targeted tracking mechanisms covering top universities, making early contact and ongoing follow-ups with domestic and international competition award winners and top students in maths and computer science departments. This includes organizing competitions, awarding prizes, summer camps, sponsoring academic activities, and more.

In such a talent system, a high-paying internship is not just a job, but also a pre-booking and binding mechanism—through real-world assessment during the internship, institutions can deeply evaluate candidates before formal recruitment.

It is worth noting that the competition is not limited to domestic institutions. As overseas quantitative firms continue to lay out in China, candidates with top overseas academic backgrounds face offers from multiple parties, further driving up overall salary expectations. To maintain competitiveness, domestic first-tier institutions have had to anchor their salary system adjustments to these external benchmarks.

Splashing Out “Scholarships”

Besides directly increasing daily wages, another display of the quantitative firms’ “money power” is in the scholarship arena.

According to Zishi Tang: Global leading market maker Optiver recently launched a PhD scholarship program. Unlike regular internship recruitment, this program directly offers real cash, providing a 100,000 yuan scholarship for selected Chinese PhD students.

According to official disclosures, the program attracted hundreds of domestic PhD applicants, but ultimately only six top scholars from Tsinghua, Peking University, Fudan, Shanghai Jiao Tong, Zhejiang University, and Renmin University won the awards.

In the selection phase, candidates had to undergo strict resume screening and participate in offline technical seminars and problem-solving evaluations.

Through this immersive experience, Optiver allows future “rare brains” to feel the real quantitative trading environment ahead of time and become deeply bound to its researchers and traders.

Simply put, this approach bypasses the traditional internship “trial period,” directly using high bonuses and top industry resources to pre-select academic rising stars who have published papers at top conferences, thus stocking up key talents for the coming AI arms race.

Designing a Growth and Promotion Path for Tomorrow’s Stars

Behind the high-paid internship is actually a precisely designed talent selection funnel.

Zishi Tang notes: A leading quantitative giant managing over 60 billion yuan recently showcased a clear progression path from “intern” to “core employee” in its latest summer internship recruitment.

From public recruitment information, the process has been deliberately simplified, requiring only 1–2 interviews for efficient advancement, aiming to quickly lock in target candidates. For those chosen, the institution offers a complete training system “from scratch,” even welcoming applicants from any academic background, seemingly with a low threshold.

But its real ambition is hidden in the conversion mechanism.

This institution claims that based on internship performance, outstanding candidates can not only receive an “offer of return employment,” but also have a chance for a “max alpha special offer.”

Obviously, through high-paying offers, the institution finally achieves the transformation from temporary employment to long-term hiring of talent.

The Future Is Promising

Once recruited, top minds formally become part of the talent pipeline in leading quantitative institutions.

Currently, top-tier quantitative firms in the industry have mostly designed differentiated promotion channels for high-potential talent, distinct from regular employees.

These channels usually follow a main line of “researcher—strategy leader—partner,” with clear nodes, corresponding increases in salary, incentives, and welfare, and direct links to strategic returns. For those who demonstrate independent strategy development capability early on, the promotion cycle can be greatly compressed, allowing faster enjoyment of proceeds.

In compensation structure, leading institutions generally use a “base salary + excess returns share” model, with sharing ratios dynamically adjusted according to strategy scale and contributions. This means that a top researcher with continuous output capability can earn far above the fixed salary figure, forming a deep bond with institutional interests.

The logic behind this mechanism is retention, not just attraction. The quantitative industry has long talent cultivation cycles and high hidden experience accumulation costs. Once a core researcher leaves, there will be obvious strategy continuity and iteration gaps. By tying personal wealth accumulation to long-term institutional development, top institutions aim to build a moat that makes it hard for top talent to leave easily.

Of course, this is only what happens later on.

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