Akuna Capital
Software Engineer Intern - Python, Summer 2027
About this role
Join Akuna Capital's 10-week summer internship program as a Python Software Engineer, working on high-performance trading systems and market data infrastructure. You'll take ownership of a meaningful project within Post Trade Technology or Data Engineering teams, collaborating with engineers and traders to solve complex problems in a fast-paced fintech environment.
What you'll do
- Design and implement solutions for post-trade technology or data engineering systems
- Work with high-performance systems processing large volumes of trading and market data
- Own and deliver a complete project from design through presentation
- Collaborate with software engineers and traders to solve complex technical problems
- Contribute to trading infrastructure, algorithms, exchange gateways, or data analysis initiatives
What they're looking for
- Python programming
- Software engineering fundamentals
- Problem-solving and algorithmic thinking
- Data structures and algorithms
- High-performance system design
- Collaboration and communication
Benefits
- Experience with cutting-edge trading technology
- Mentorship from experienced engineers and traders
- Exposure to fintech and derivatives markets
- Opportunity to present work and accomplishments
- Team-based learning environment
Opens the official application on the employer’s site. No login required.
Akuna Capital
Akuna Capital is an options market-maker and trading firm that operates 24/7 across crypto and traditional markets globally. The company is hiring Systems Engineers, Software Engineers, Security Engineers, and Data Engineers to build and maintain low-latency trading infrastructure, core trading systems, large-scale data pipelines, and secure technical infrastructure.
View all jobs at Akuna CapitalLikely interview questions
- Walk us through a complex problem you've solved using Python—what was your approach and how did you optimize it?
- Describe your experience with data structures and algorithms. How would you approach processing large volumes of data efficiently?