AI & Trading Engineer

Galaxy•New York, NY

3h•$200,000 - $250,000

About The Position

We are building an advanced, AI-driven trading platform and are looking for a senior engineer who thrives at the intersection of machine learning, quantitative finance, and production systems. This role is ideal for someone who has worked on real trading or research platforms and wants to apply cutting-edge AI techniques to complex, high-impact problems in the financial markets. You will help design and scale systems that power the full lifecycle of systematic trading, from data and modeling to simulation, deployment, and live performance analysis.

Requirements

8+ years of experience in software engineering, machine learning engineering, or quantitative systems.
Strong programming skills in Python, plus experience with C++, Rust, or Java.
Hands-on experience with backtesting frameworks, research platforms, or trading systems used in real-world decision-making.
Solid understanding of machine learning applied to noisy, non-stationary time-series data.
Familiarity with systematic trading concepts: signal research, execution, portfolio construction, and risk management.
Experience building and operating production-grade systems.

Nice To Haves

Background as a quant developer or research engineer in a systematic trading environment.
Knowledge of market microstructure, order types, and transaction cost modeling.
Experience with distributed systems, GPUs, or large-scale ML pipelines.
Advanced degree in CS, Math, Statistics, Physics, or a related field.

Responsibilities

Build and evolve core components of an AI-enabled systematic trading platform.
Develop and maintain robust backtesting and simulation infrastructure, accounting for transaction costs, slippage, and market impact.
Partner closely with quantitative researchers and traders to productionize trading strategies and models.
Design tools and workflows that significantly improve research speed, reliability, and reproducibility.
Apply machine learning techniques to financial time-series data, including feature engineering, model training, and evaluation.
Implement monitoring, post-trade analytics, and performance attribution to support continuous improvement.
Provide technical leadership through architecture decisions, code reviews, and mentorship.