Quantitative Developer, Quantitative Strategies

Millennium•New York, NY

1d•$150,000 - $200,000•Onsite

About The Position

We are seeking a highly skilled, entrepreneurial Quantitative Developer to join an existing collaborative quantitative trading pod. This is a hands-on role at the intersection of technology, data, research, and trading, with direct exposure to the Senior Portfolio Manager and quantitative researchers. The role spans the full systematic trading stack, with a particular focus on research infrastructure, data systems, signal deployment, and production monitoring.

Requirements

Strong Python engineering skills, with the ability to write clean, scalable, production-quality code
Experience with performance optimization in Python and with parallel / distributed workloads
Familiarity with tools such as Kubernetes, Ray, Dask, Polars, Slurm, or similar distributed compute / orchestration frameworks
Experience with SQL; familiarity with modern data warehouses such as Snowflake is a plus
Strong Linux experience
Solid understanding of system design, design patterns, and data architecture
Excellent communication, analytical, and problem-solving skills, with the ability to quickly understand and implement complex quantitative workflows
3+ years of experience as a quantitative developer, research engineer, or software / data engineer, ideally in a systematic trading or financial context
Experience building or supporting research platforms, simulation frameworks, or quantitative data infrastructure
Experience creating, organizing, and maintaining custom datasets and production-grade data pipelines
Experience supporting the deployment, monitoring, and maintenance of live research outputs or trading models
Experience working closely with researchers in a fast-paced, iterative environment

Responsibilities

Own and continuously improve the team’s research platform, including the backtesting framework, simulation environments, and caching / compute layers
Build and maintain tooling that enables researchers to develop, test, and deploy signals efficiently
Integrate Agentic AI workflows where they can improve productivity, model development, or operational robustness
Design, organize, and maintain large-scale datasets and data pipelines used across research and production
Optimize and support the team’s interfaces with central and external systems, including execution, risk monitoring, and compute / resource management
Help productionize and monitor trading signals, ensuring robustness, observability, and operational reliability
Partner closely with researchers and the SPM to translate research needs into scalable engineering solutions