Location: Miami, USA Remote: No, hybrid/in-office preferred Willing to Relocate: Yes, New York/ San Francisco ideally

Technologies: Python, C++ (intermediate), SQL (intermediate), PyTorch, TensorFlow, Hugging Face, XGBoost, Statsmodels, CUDA/GPU acceleration, Docker, AWS, LangChain, RAG, Git, FastAPI, Data Analysis, Machine Learning, Deep learning - Model training, evaluation and research, Quant Research, Algorithmic Trading Strategies

Résumé/CV: https://docs.google.com/document/d/1O3_DgR8TDWRQ4_WVs_G6jEjL...

Email: shubham.singh@nyu.edu

I’m Shubham Singh, a Quantitative Researcher & AI Engineer with a strong background in systematic trading, LLM research, and large-scale infrastructures.

Previously worked at GoQuant on high-frequency and mid-frequency trading strategies, predictive models for time series data, LLM integration for Trade analysis, and played a key role in developing firm's monetization and product launches. I’ve also co-authored research at the AI Institute of South Carolina, including a paper accepted at EMNLP 2025.

I've also worked on interesting projects like Qapture, a financial analysis platform combining alternative, macro, and fundamental data with AI-driven insights for retail investors. And Autodoc, a tool to generate automatic documentation of your code.

I'm ideally looking for engineering roles in AI engineering/research, software development or quantitative trading.