VP Applied AI - Deep Learning

Goldman Sachs • New York, NY

20d • $115,000 - $300,000

About The Position

Goldman Sachs Applied AI develops advanced machine learning, statistical modeling, and quantitative research capabilities for complex problems across global financial markets. The team combines modern AI methods, rigorous statistical inference, high-dimensional market data, and domain expertise, working at the intersection of artificial intelligence, quantitative finance, and market microstructure.

As a Senior Applied AI Research Engineer, the successful candidate will conduct original research in deep learning, time-series modeling, and quantitative finance. The role includes formulating research questions, developing predictive and representation-learning methods, and evaluating statistical and economic relevance using large-scale financial datasets. The position owns research end to end: framing questions, curating and engineering data, designing model architectures, running large-scale training experiments, building evaluation processes, and partnering with trading desks and engineers to ground research in real market problems.

The role also contributes reusable research assets and reproducible methods, mentors junior researchers and engineers, communicates research findings and limitations to technical and business stakeholders, and follows the firm's model-risk-management, data-governance, privacy, ethics, and responsible-AI standards.

Requirements

• Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, or a related quantitative discipline.
• At least five years of professional experience in machine learning, quantitative research, applied research, data science, or a closely related field.
• Strong research background in one or more of deep learning and representation learning; time-series forecasting and sequential modeling; quantitative finance; or market microstructure.
• Demonstrated ability to formulate and independently execute rigorous research using large, complex, and high-dimensional datasets.
• Ability to communicate complex research ideas, empirical findings, and methodological trade-offs clearly and effectively.
• Ability to work independently on open-ended research questions while collaborating effectively with traders and engineers.

Nice To Haves

• Experience with Transformer-based models, probabilistic deep learning, or other modern sequence architectures.
• Experience with multivariate forecasting, cross-sectional prediction, or high-frequency market data.
• Research experience in quantitative trading, systematic investing, market microstructure, electronic execution, market making, statistical arbitrage, or financial econometrics.
• Publications or demonstrated research contributions in leading machine-learning, artificial intelligence, statistics, optimization, or quantitative-finance venues.
• Demonstrated ability to connect methodological advances in AI with robust and economically meaningful results in financial markets.

Responsibilities

• Research 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.
• Design rigorous evaluations of predictive performance, robustness, calibration, and economic relevance using walk-forward testing, leakage controls, regime analysis, and statistical validation.
• Partner with quantitative researchers, strategists, and trading stakeholders on research questions involving alpha generation, signal generation, return and volatility forecasting, liquidity, market microstructure, execution, and portfolio construction.
• Translate research results into economically meaningful, risk-aware, and capacity-conscious quantitative insights.
• Contribute reusable research artifacts, including pretrained models, datasets, benchmarks, evaluation protocols, and documentation.
• Mentor junior researchers and engineers and review research design, empirical methodology, and research code.
• Communicate research findings, assumptions, limitations, and recommendations to senior technical and business stakeholders.
• Conduct research in accordance with model-risk-management, data-governance, privacy, ethics, and responsible-AI standards.
• Document research methodology, data provenance, model assumptions, validation results, and limitations.

Benefits

• Opportunity to work on frontier financial AI research with potential commercial impact.
• Access to proprietary, high-quality cross-asset market data and computational resources for large-scale training and empirical research.
• Participation in a rigorous Applied AI research culture emphasizing scientific rigor, robust out-of-sample evaluation, reproducibility, and economically meaningful results.
• Opportunity to collaborate with quantitative researchers, strategists, and trading stakeholders across businesses.
• Opportunity to contribute to internal research forums and, subject to firm policies and approvals, external publications and engagement with the research community.
• Opportunity to help shape the team's research priorities and future direction.
• Potential eligibility for a discretionary bonus for active employees as of fiscal year-end.
• Training and development opportunities, firmwide networks, benefits, wellness and personal finance offerings, and mindfulness programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree