Quantitative Developer - Evolve Group : Job Details

Quant Developer/Software Engineer - Leading Trading Firm

Location: New York

Primary Technology: Python

Overview:

We are a leading quantitative hedge fund in NYC seeking exceptional Software Engineers and Developers to design, build, and optimize cutting-edge trading systems and research platforms. Our firm applies advanced reinforcement learning (RL), causal inference, and high-performance computing (HPC) to drive profitable trading strategies in global markets.

This role provides a unique opportunity to work at the intersection of AI research, quantitative finance, and large-scale engineering, collaborating with top-tier quantitative researchers and finance professionals to push the boundaries of systematic trading.

We are looking for engineers with experience working alongside top AI labs, professors, and research institutions, applying state-of-the-art deep learning, reinforcement learning, natural language processing (NLP), and convex optimization techniques to financial markets.

We Are Looking for Engineers with Experience in Any of the Following Areas:

Reinforcement Learning (RL) & Machine Learning:

• Experience with deep RL algorithms (TRPO, SAC, PPO, DQN) and their application to decision-making systems, market-making, or portfolio optimization.
• Background in supervised and unsupervised learning for predictive modeling.
• Familiarity with transformer models, sequence modeling, and time-series forecasting.

Causal Inference & Statistical Learning:

• Experience with Bayesian networks, structural causal models, heterogeneous treatment effect estimation.
• Understanding of counterfactual reasoning and causal AI in finance or decision systems.

Optimization, Operations Research & High-Performance Computing (HPC):

• Expertise in convex optimization, stochastic control, dynamic programming for asset allocation and trade execution.
• Experience with parallel computing, GPU acceleration (CUDA, TensorRT), distributed training (Ray, Kubernetes).

Quantitative Finance & Market Microstructure:

• Background in algorithmic trading, market impact modeling, and execution optimization.
• Familiarity with high-frequency trading (HFT), order book dynamics, and liquidity modeling.
• Experience working with financial time-series data, derivatives pricing, or portfolio risk models.

Large-Scale Infrastructure & Data Engineering:

• Building high-performance data pipelines, streaming architectures, and real-time analytics systems.
• Experience in distributed computing frameworks (Apache Spark, Dask, Ray, Kafka).

Requirements:

Experience:

• 3-15 years of industry experience in software engineering, AI/ML research, or quantitative development.
• Proven track record of working with or learning from top AI professors, labs, and research institutions, such as:
• Berkeley AI Research (BAIR), MIT CSAIL, Stanford AI Lab, Princeton ORFE, Columbia Causal AI Lab, DeepMind, Google Brain, OpenAI, JPMorgan AI Research.
• Experience collaborating with leading academic experts, including Pieter Abbeel, Michael I. Jordan, Susan Athey, Andrew W. Lo, René Carmona, Manuela Veloso, Stephen Boyd etc.

Technical Expertise:

• Strong programming skills in Python.
• Deep knowledge of data structures, algorithms, and distributed computing for high-performance systems.
• Experience implementing machine learning, reinforcement learning, optimization, or causal inference models in financial or technical domains.
• Familiarity with quantitative finance concepts, market microstructure, execution algorithms, and portfolio risk management.
• Experience working with high-performance computing (HPC), GPU acceleration, and large-scale AI systems.

Education:

• Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Applied Mathematics, Electrical Engineering, Operations Research, or a related quantitative field from a top-tier university.
• Strong academic record (GPA 3.5+ preferred).
• Coursework or research experience in AI/ML, reinforcement learning, optimization, quantitative finance, or computational statistics.

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