Quantitative Engineer

Location: Jersey City, NJ

Duration: Contract to Hire.

Job Description: We are seeking a Qualitative Engineer, Massive-Scale Pricing Engine to join our dynamic team. The ideal candidate will have strong experience in Python, distributed systems, cloud/grid computing, and market risk pricing platforms and a proven ability to design, scale, and operate high-performance pricing grids that deliver fast and reliable valuation at enterprise scale.

Responsibilities: Architect, build, and manage a large-scale distributed compute grid on AWS or GCP for financial pricing models. Design and implement orchestration for distributing millions of pricing tasks across extensive CPU and GPU fleets. Deploy, manage, and version a library of quantitative pricing models for optimal performance in distributed environments. Monitor and optimize performance, cost, and resource utilization to drive continuous efficiency gains. Collaborate with quantitative development teams to integrate new and updated models into production workflows. Engineer robust data logistics to deliver market data, trade data, and configurations at runtime. Ensure high availability, resiliency, and adherence to recovery time objectives for the pricing engine.

Required Skills & Qualifications: 10+ years designing, building, and operating applications on large-scale compute grids. Expert hands-on experience with AWS or GCP, including batch processing, containers, and serverless services. Deep expertise with Docker and Kubernetes. Strong programming skills in Python and C++ for high-performance computing. Background in distributed systems, performance tuning, and infrastructure as code. Experience with large-scale Monte Carlo simulations, VaR, or XVA pricing grids in financial services is desirable. Excellent problem-solving skills and the ability to operate in high-pressure environments. Clear communication skills with stakeholders in quantitative research, trading, and risk.

Preferred Skills: Experience with market risk platforms, grid schedulers, and pricing engines. Familiarity with cost optimization and observability in large cloud estates.