Title: Lead Platform Engineer - Market Risk & Pricing Location: Jersey City, NJ (Hybrid, 2-3 days onsite in a week) Duration: Fulltime
Your Role and Impact:
As the lead for the Pricing Engine, you are the master of massive-scale computation. You will take the sophisticated pricing models developed by our top quants and operationalize them on a colossal grid. Your primary mission is to ensure that millions of trades can be re-valued against thousands of historical market scenarios with extreme speed, efficiency, and rock-solid stability.
Your impact is at the core of our risk valuation capability. You will architect the system that answers the most fundamental question in risk: "What is it worth, right now, under this scenario?" The performance and reliability of the platform you build will directly determine the firm's ability to manage risk and meet its most critical regulatory obligations.
Key Responsibilities:
Architect, build, and manage a massive-scale, distributed compute grid on public cloud platforms (AWS, Google Cloud Platform) 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 Google Cloud Platform), 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.
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