VP Applied AI Deep Learning

Job DescriptionWHAT WE DO:At Goldman Sachs, our Engineers don't just make things - we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.Engineering, which is comprised of our Technology Division and global strategists groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.APPLIED AI AT GOLDMAN SACHS:Goldman Sachs Applied AI develops advanced machine learning, statistical modeling, and quantitative research capabilities for complex problems across global financial markets. We combine modern AI methods, rigorous statistical inference, high-dimensional market data, and domain expertise to develop innovative quantitative models and research insights.Our team operates at the intersection of artificial intelligence, quantitative finance, and market microstructure. We seek to advance the scientific understanding and practical application of machine learning for forecasting, market modeling, execution, liquidity, and systematic trading.THE ROLE:Title: Applied AI - Vice President Location: New York, NY Division: Engineering - Applied AIAs a Senior Applied AI Research Engineer, you will conduct original research at the frontier of deep learning, time-series modeling, and quantitative finance. You will formulate important research questions, develop novel predictive and representation-learning methods, and evaluate their statistical and economic relevance using large-scale financial datasets.You will own research to apply problems end to end: framing the question, curating and engineering the data, designing the model architecture, running large-scale training experiments, building the evaluation harness, and partnering closely with trading desks and engineers to ensure that research is grounded in real market problems and can inform impactful quantitative applications.WHAT YOU WILL BE WORKING ON: * Deep Learning, Transformers, and Time-Series Foundation Models: Conduct research on deep-learning methods for financial time-series forecasting, representation learning, and generative modeling. Develop Transformer-based and time-series foundation models using large-scale pre-training, self-supervised learning, transfer learning, and adaptation across assets, market regimes, and forecasting horizons. * Scientific Evaluation and Applied Quantitative Research: Design rigorous studies of predictive performance, robustness, calibration, and economic relevance using walk-forward testing, leakage controls, regime analysis, and statistical validation. Partner with quantitative researchers and trading stakeholders on research in alpha generation, return and volatility forecasting, liquidity, market microstructure, execution, and portfolio construction. * Applied quantitative research: Partner with quantitative researchers, strategists, and trading stakeholders to formulate high-value research questions in alpha research, signal generation, return and volatility forecasting, liquidity modeling, market microstructure, execution, and portfolio construction. Translate research results into economically meaningful, risk-aware, and capacity-conscious quantitative insights. * Research assets and reproducibility: Contribute reusable research artifacts, including pretrained models, datasets, benchmarks, evaluation protocols, and documentation, to support reproducible research and collaboration across asse