Quant Infrastructure Engineer

Plantrs Quant Infrastructure Engineer

About the Role

As a Quant Infrastructure Engineer you will play a critical role in shaping the backbone of a high-performance machine learning ecosystem. You will design and scale the infrastructure that powers model development-from large-scale dataset generation to real-time production deployment.

Working at the intersection of machine learning data engineering and distributed systems you will collaborate closely with quantitative researchers and engineers to ensure seamless workflows across the ML lifecycle. This is an opportunity to directly impact live trading systems where performance reliability and speed are essential.

You will work with modern technologies including Python Kubernetes Ray and Airflow to build robust scalable systems that support cutting-edge research and production-grade ML applications.

Responsibilities

• Design optimize and evolve infrastructure supporting distributed model training experiment tracking and large-scale data processing
• Develop and enhance internal Python tools to streamline workflows and improve usability across teams
• Build and automate reliable data pipelines for dataset generation feature engineering and version control
• Enable efficient experimentation and deployment through automated testing CI/CD pipelines and scalable environments
• Improve and expand the feature generation system including developing a feature store with clear definitions and automated processes

Your Skills & Experience

• Strong scientific mindset with a natural curiosity for solving complex problems
• Solid foundation in computer science fundamentals including data structures and algorithms
• Experience working with large-scale or out-of-core datasets
• Proficiency in Python particularly in data preprocessing ETL pipelines and distributed systems
• Familiarity with strongly typed languages such as C# C/C or Rust
• Hands-on experience in production environments supporting machine learning applications
• Experience with distributed computing frameworks such as Spark Ray or Dask
• Familiarity with containerization and orchestration tools such as Docker Compose or Kubernetes

Why You Should Join

• Work on Real Impact Systems: Your contributions will directly influence real-time trading decisions in a high-performance environment
• Cutting-Edge Technology Stack: Build and scale systems using modern tools and frameworks at the forefront of ML and distributed computing
• Ownership and Autonomy: Operate in a flat structure where your ideas are valued and decision-making is fast and collaborative
• Strong Growth Environment: Join a high-growth organization where your work is visible recognized and impactful
• Collaborative Culture: Be part of a highly skilled supportive team that values knowledge sharing and continuous learning
• Flexible Work Model: Enjoy a hybrid setup with a modern office in Copenhagen
• Comprehensive Benefits: Competitive compensation package including pension health insurance and 30 days of vacation
• Engaging Team Environment: Regular company events offsites and team celebrations that foster strong connections

Nice to Have

• End-to-end experience with the machine learning lifecycle including MLOps practices
• Experience with frameworks such as PyTorch TensorFlow XGBoost or CatBoost
• Understanding of algorithmic trading concepts
• Advanced degree (PhD or MSc) in engineering computer science mathematics or physics