10 years of experience in designing and running applications on large-scale compute grids
Expertise with major public cloud providers (AWS or GCP) and their services
Deep knowledge of containerization (Docker) and orchestration (Kubernetes)
Strong programming in C++ and Python for high-performance computing
Experience in financial applications (e.g. Monte Carlo simulations, VaR calculations) is a plus
Degree in Computer Science, Engineering or related field
Background in distributed systems and infrastructure-as-code
Exceptional problem-solving abilities in high-pressure environments
Strong communication skills for collaborating with various teams
Responsibilities
Architect and manage a large-scale distributed compute grid for financial pricing
Design the orchestration layer for efficient distribution of pricing tasks
Deploy and version control quantitative pricing models in a distributed setting
Monitor and optimize cloud grid performance, cost, and resource utilization
Collaborate with quantitative teams to integrate pricing models into production
Engineer data logistics for market and trade data availability
Ensure the pricing engine is resilient and meets recovery time objectives
Benefits
Comprehensive health and wellness programs
Flexible working hours and remote work options
Professional development opportunities
Retirement savings plan with company matching
Generous paid time off and holidays
Full Job Description
Key Responsibilities
Architect, build, and manage a massive-scale distributed compute grid on public cloud platforms (AWS, GCP) for running financial pricing models.
Design and implement the orchestration layer responsible for distributing millions of pricing tasks efficiently across hundreds of thousands of CPU/GPU cores.
Deploy, manage, and version control a diverse library of quantitative pricing models, ensuring they run optimally in a distributed environment.
Obsessively monitor and optimize the performance, cost, and resource utilization of the cloud grid, driving continuous efficiency improvements.
Collaborate with quantitative development teams to seamlessly integrate new and updated pricing models into the production grid.
Engineer the data logistics to ensure that the correct market data, trade data, and model configurations are available for every calculation at runtime.
Ensure the pricing engine is highly available, resilient, and capable of meeting stringent recovery time objectives.
What We're Looking For
10 years of professional experience with a proven track record of designing, building, and running applications on massive-scale compute grids.
Expert-level hands-on experience with at least one major public cloud provider (AWS or GCP), including their batch processing, container, and serverless offerings.
Deep expertise in containerization and orchestration technologies (Docker, Kubernetes).
Strong programming skills in languages common to high-performance computing such as C++ and Python.
Prior experience in a similar role within the financial industry (e.g., running large-scale Monte Carlo simulations, VaR calculations, or XVA pricing grids) is highly desirable.
A degree in Computer Science, Engineering, or a related technical field.
A strong background in distributed systems, performance tuning, and infrastructure-as-code principles.
Exceptional problem-solving skills with an ability to diagnose and resolve complex issues in a high-pressure, large-scale environment.
Excellent communication skills and the ability to work effectively with quantitative research, trading, and risk management teams.
