Senior AI/ML Quant Research Engineer, Applied AI

Who We Are

The Applied AI team at Goldman Sachs operates at the intersection of artificial intelligence, quantitative finance, and technology. Our mandate is to research, develop, and deploy cutting-edge AI/ML models that drive commercial impact and solve the most complex predictive challenges across the firm. We function as a center of excellence, partnering with trading, sales, and engineering divisions to pioneer next-generation quantitative technologies that redefine our revenue-generating capabilities.

Your Impact

As a Senior AI/ML Quant Research Engineer, Applied AI, you will be at the forefront of financial innovation. You will have the unique opportunity to apply your deep expertise in machine learning and quantitative analysis to high-impact projects, from developing sophisticated alpha-generation models to engineering state-of-the-art market-making and pricing systems. This role offers end-to-end ownership, from initial research and prototyping to deploying scalable, robust models into our production trading environment. You will tackle the unique challenges of applying AI in the high-stakes, non-stationary world of quantitative trading and help shape the future of finance.

Principal Responsibilities

• Model Architecture & Implementation: Spearhead the end-to-end lifecycle of AI/ML models, from initial research and ideation through to production deployment, with a clear focus on driving measurable commercial impact.
• Advanced Predictive Modeling: Design, train, and validate novel models for predictive tasks in complex financial time series, including deep learning, reinforcement learning, and state-space models.
• Explainable AI (XAI) & Governance: Integrate and advance state-of-the-art XAI methodologies to ensure model transparency, interpretability, and robustness. Satisfy the rigorous demands of internal model validation, risk management, and regulatory frameworks.
• MLOps & Engineering Excellence: Engineer and maintain high-quality, production-grade code and resilient data pipelines for high-volume, low-latency financial data. Adhere to and promote best practices in MLOps for versioning, containerization, continuous integration/deployment, and real-time monitoring.

Core Qualifications

• A Ph.D. or Master’s degree in a quantitative discipline such as Computer Science, Statistics, Quantitative Finance, Mathematics, Physics, or Electrical Engineering.
• Expert-level programming proficiency in Python and deep experience with its scientific computing and machine learning ecosystem (e.g., NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• A profound theoretical and applied understanding of machine learning techniques, including LLMs, deep learning architectures, reinforcement learning, probabilistic models, and classical statistical methods.
• Proven ability to independently conduct research, manage complex datasets, and solve challenging, open-ended problems with a data-driven approach.
• Exceptional communication and interpersonal skills, with the ability to articulate complex technical concepts to both specialist and non-specialist audiences.

Preferred Qualifications

• Min. 3 years (for Associate) / 8 years (for VP) of distinguished professional or academic research experience, demonstrated by a track record of building and fine-tuning large-scale deep learning models (e.g., Transformers) for sequential or time-series data.
• Prior experience in a quantitative role at a leading buy-side or sell-side institution (e.g., quantitative trading, statistical arbitrage, high-frequency market making).
• Direct, hands-on experience applying foundation models (e.g., LLMs) and transfer learning techniques to novel, non-NLP domains.

Key skills/competency

• AI/ML
• Quantitative Finance
• Deep Learning
• Reinforcement Learning
• Time Series Analysis
• MLOps
• Python
• PyTorch/TensorFlow
• Explainable AI (XAI)
• Financial Modeling