C++ Software Engineer at WorldQuant

Budapest, Hungary

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Financial Services, Quantitative Finance, TechnologyIndustries

Requirements

A minimum of 5 years of writing production-quality code in C++ on Linux platform
Good understanding of Python language with development experience
Strong knowledge of Unix/Linux fundamentals
Ability to develop large-scale, distributed systems
Strong understanding of data structures, algorithms, high-performance application design, and concurrency patterns
Self-motivated and able to work independently with minimal direction
Finance experience is a plus but not essential

Responsibilities

Participate in the development and maintenance of our large-scale, distributed storage platform
Organize the day-to-day work of the team working on the same software component
Collaborate closely with stakeholders to comprehend business requirements, analyze issues, and propose solutions that align with our business goals
Provide technical support for platform-related issues, which includes diagnosing the root causes of technical problems and suggesting appropriate solutions

Skills

Key technologies and capabilities for this role

C++LinuxPythondistributed systemsstorage platformsproduction code

Questions & Answers

Common questions about this position

What compensation and benefits are offered for this role?

WorldQuant offers a competitive compensation package, including premium private health insurance, life insurance with savings plan, Employee Assistance Program, and fully covered sick leave.

Is this position remote or does it require office work?

This information is not specified in the job description.

What key skills are required for the C++ Software Engineer role?

Candidates need a minimum of 5 years of production-quality C++ code on Linux, good understanding of Python, strong Unix/Linux fundamentals, ability to develop large-scale distributed systems, and knowledge of data structures, algorithms, high-performance design, and concurrency.

What is the company culture like at WorldQuant?

The culture pairs academic sensibility with accountability, encourages open thinking, challenging conventions, continuous improvement, and collaboration in a relaxed yet intellectually driven environment.

What makes a strong candidate for this position?

Strong candidates demonstrate intellectual horsepower, outstanding talent, self-motivation, ability to work independently, and experience building large-scale systems; finance experience is a plus.

WorldQuant

Quantitative asset management using algorithms

About WorldQuant

WorldQuant is a quantitative asset management firm that focuses on managing investments for institutional clients like pension funds and sovereign wealth funds. The firm uses data and predictive algorithms to analyze financial markets and identify investment opportunities. Its approach involves algorithmic trading, where mathematical models guide investment decisions. Unlike many competitors, WorldQuant encourages a culture of experimentation and innovation among its employees, allowing everyone to contribute ideas regardless of their position. The company's goal is to generate returns for its clients while maintaining a commitment to equal opportunity in the workplace.

Greenwich, ConnecticutHeadquarters

2007Year Founded

$148.5MTotal Funding

N/ACompany Stage

Quantitative Finance, Financial ServicesIndustries

1,001-5,000Employees

Benefits

Performance Bonus

Flexible Work Hours

Risks

Increased competition from AI-driven investment firms like ADIA.

Regulatory scrutiny on algorithmic trading practices is increasing globally.

Market volatility challenges the performance of algorithmic trading models.

Differentiation

WorldQuant employs over 1,000 professionals across 27 global offices.

The firm uses predictive algorithms to manage assets and generate client returns.

WorldQuant emphasizes equal opportunity, allowing all employees to contribute meaningfully.

Upsides

Increased focus on alternative data sources is gaining traction in quantitative finance.

Machine learning integration in portfolio management allows better market trend predictions.

Quantum computing offers potential for faster, complex calculations in algorithmic trading.

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