Company Name: QSentia

Job Details: Job detailsHere’s how the job details align with your profile.Job typePart-time 

Job Url: https://www.indeed.com/viewjob?cmp=QSentia&t=Quantitative+Developer&jk=3924b9a2bbffaa47&q=ai&xpse=SoCz67I3sTNvbVxUMh0LbzkdCdPP&xfps=f79ae46f-fc98-45b5-9828-47d3f62f1b2c&xkcb=SoDe67M3sTNQnj0rH50PbzkdCdPP&vjs=3

Job Description: About Us
We are building a next-generation hedge fund platform that integrates reinforcement learning (RL) with large language models (LLMs) to create a state-of-the-art portfolio management system. Our mission is to combine cutting-edge quantitative research with scalable AI systems to achieve superior risk-adjusted returns during periods of market volatility.
The Opportunity
As a Senior Quantitative Developer, you will be at the forefront of designing and implementing our proprietary RL + LLM Alpha Generation and Risk Management framework. This role combines quantitative finance expertise, advanced machine learning, and software engineering to deliver a best-in-class research and trading platform.
What You’ll Do
 Design and implement RL-based portfolio optimization models (e.g., DDPG, TD3, PPO) with a focus on adaptive risk management and regime detection.
 Develop and integrate LLM-driven alpha signals, enabling the system to extract hidden insights from multimodal data sources (earnings calls, filings, news, social sentiment, market structure).
 Architect a scalable pipeline that combines real-time alpha vectors with RL-driven portfolio allocation and trade execution.
 Build and maintain walk-forward and event-driven backtesting frameworks with realistic transaction cost and slippage models.
 Implement multi-metric validation frameworks beyond Sharpe/Sortino, including max drawdown, Calmar, CVaR, and risk-concentration metrics.
 Collaborate with researchers and portfolio managers to translate quantitative research into production-grade trading systems.
 Optimize performance for GPU-accelerated training and efficient data pipelines (SQL, cloud, or hybrid).

What We’re Looking For

 7+ years of experience in quantitative development, algorithmic trading, or applied ML research in finance.
 Strong background in machine learning / reinforcement learning (PyTorch, TensorFlow) applied to portfolio management or trading strategies.
 Experience designing actor-critic RL frameworks (DDPG, TD3, PPO, SAC) with risk-adjusted reward functions.
 Deep understanding of financial markets, risk models, and portfolio theory.
 Proficiency in Python (NumPy, Pandas, PyTorch) and SQL/NoSQL databases; C++ or Rust is a plus.
 Hands-on experience with LLMs (OpenAI, Claude, Gemini, etc.), natural language processing, or multimodal AI for financial signal extraction.
 Proven ability to design backtesting engines and eliminate lookahead bias with point-in-time datasets.
 Strong communication skills and ability to work with PMs, researchers, and technologists.

Nice to Have

 Experience with real-time market data APIs (Polygon, Bloomberg, Refinitiv, etc.).
 Knowledge of options markets and derivatives pricing.
 Familiarity with distributed computing frameworks (Ray, Dask, Spark) for large-scale research.
 Prior experience at a hedge fund, HFT shop, or asset manager in a quant dev or quant research role.

Why Join Us

 Shape the future of AI-driven portfolio management at an early stage.
 Work on a frontier problem: combining reinforcement learning with large language models for trading.
 Join a high-performance culture where innovation and rigor drive investment outcomes.
 Competitive compensation: Founder Equity + performance-linked bonus + equity participation.

Job Type: Part-time
Base Pay: From $1.00 per year
Expected hours: 7 – 14 per week
Work Location: Remote