Trading Systems Performance Engineer

Supergrads; Amsterdam; On-site; EUR 75,000 - 110,000; Full time

Job summary

Supergrads is seeking a technically curious Performance Engineer to improve performance in complex electronic trading environments. You will combine systems engineering, quantitative analysis and networking to understand real-world behavior and reduce latency.

The role emphasizes data-driven hypothesis testing, experimentation and delivering tangible improvements across the technology stack with cross-disciplinary collaboration.

Qualifications

• Strong knowledge of computer architecture, networking and/or Linux.
• Experience with reverse engineering or black-box analysis of complex systems.
• Analytical mindset with a focus on data-driven experimentation.
• Proficient in Python for data analysis and system investigation.
• Understanding of sophisticated networking environments and stacks.
• Independent, proactive problem-solving with cross-disciplinary collaboration.

Responsibilities

• Investigate complex systems using black-box analysis and structured experiments.
• Perform data-driven research on large datasets to uncover patterns.
• Identify latency and efficiency improvements across the tech stack.
• Build lightweight prototypes and proofs-of-concept for new approaches.
• Collaborate with traders, software engineers, hardware specialists and infra teams.
• Analyze network behavior with statistical methods and experiments.
• Own investigations from question through analysis, experimentation and recommendations.

Skills

Computer Architecture

Networking

Linux

Reverse Engineering

Python for Data Analysis

Analytical Mindset

Data-driven Research

Independent Problem Solving

Cross-functional Collaboration

Job description

Supergrads is seeking a technically curious Performance Engineer to improve performance in complex electronic trading environments. You will combine systems engineering, quantitative analysis and networking to understand real-world behavior and reduce latency.

The role emphasizes data-driven hypothesis testing, experimentation and delivering tangible improvements across the technology stack with cross-disciplinary collaboration.