Location: Preference for Dubai
Reporting to: Head of Data and AI
About AlgoQuant Asset Management
At AlgoQuant, we're building the future of digital asset management, grounded in rigorous research, world-class technology, and a relentless focus on performance.
We began as a proprietary trading firm, developing sophisticated algorithmic strategies and operating in some of the most complex and fast-moving markets. That DNA remains at our core, but today we are evolving into a fully remote, globally distributed investment management business. This transformation reflects a broader ambition: to scale our edge, deliver institutional-grade results, and set new standards for the industry.
Our quantitative environment is built to empower innovation, combining vast data capabilities, disciplined model development, and highly automated execution. Risk is embedded in every layer of our thinking, with robust measurement, control, and scenario analysis integrated into our systems and decision-making. Technology is not just a tool for us, it’s a core competency and a competitive advantage.
Role Overview
You will join a highly skilled Technology team responsible for building and maintaining the core systems and data pipelines that power trading, research, and analytics across the firm. You’ll work at the intersection of data engineering, real-time systems, and applied AI, collaborating closely with traders, quants, and other engineers.
Key Responsibilities:
- Design, build, and maintain shared trading libraries and modules used for:
- Feature engineering
- Data pipelines
- Back-testing frameworks
- Common platform services
- Develop and operate real-time, batch, and streaming pipelines for high-volume market data
- Ensure data quality and reliability by implementing validation, monitoring, and alerting across the data ecosystem
- Partner closely with Traders, Quants, Mid-Office, and Technology stakeholders to gather requirements and deliver robust, well-tested solutions
Qualifications & Experience
Must-Have Experience:
- 7+ years of professional software engineering experience
- Strong experience in Python and C#
- Experience with C++ and/or Rust is a plus
- Hands-on experience with distributed data processing frameworks
- e.g. Apache Spark, Ray, Slurm, or similar
- Experience with modern data lakehouse technologies
- e.g. Apache Iceberg, Delta Lake, or similar
- Experience with event-driven and data-platform tooling
- Kafka, Kubernetes, Docker, Airflow, or similar
- Practical experience with machine learning workflows
- PyTorch, TensorFlow, and/or inference frameworks
- Experience working with at least one major cloud provider
- AWS, GCP, Azure, or similar
- Comfortable using AI-assisted development tools
- e.g. Claude Code, GitHub CLI, Codex, or similar
Nice to have:
- Experience with financial market data and trading systems
- Familiarity with cryptocurrency exchanges, on-chain data, options, or alternative data sources
- Background in low-latency or high-throughput systems
Why Join AlgoQuant?
- Work at the forefront of quantitative trading and AI-driven research in crypto markets.
- Lead a high-caliber technology team, shaping the future of our trading infrastructure.
- Collaborate with world-class researchers, traders, and engineers to drive technological excellence.
- Competitive compensation, including performance-based incentives and long-term upside potential.
Top Skills
What We Do
At AlgoQuant, we're building the future of digital asset management; grounded in rigorous research, world-class technology and a relentless focus on performance.
We began as a proprietary trading firm, developing sophisticated algorithmic strategies and operating in some of the most complex and fast-moving markets. That DNA remains at our core, but today we are evolving into a fully remote, globally distributed Investment Management business. This transformation reflects a broader ambition: to scale our edge, deliver institutional-grade results, and set new standards for the industry.
Our quantitative environment is built to empower innovation, combining vast data capabilities, disciplined model development, and highly automated execution. Risk is embedded in every layer of our thinking, with robust measurement, control, and scenario analysis integrated into our systems and decision-making. Technology is not just a tool for us, it’s a core competency and a competitive advantage.
