Job description

Job Description WHAT 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 AI As 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 asset classes and quantitative research teams. • Technical leadership and communication: Mentor junior researchers and engineers; provide thoughtful reviews of research design, empirical methodology, and research code; and communicate findings, assumptions, limitations, and recommendations clearly to senior technical and business stakeholders. • Research governance and responsible AI: Conduct research in accordance with the firm’s model-risk-management, data-governance, privacy, ethics, and responsible-AI standards. Maintain clear documentation of research methodology, data provenance, model assumptions, validation results, and limitations SKILLS AND EXPERIENCE WE ARE LOOKING FOR: Required • Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, or a related quantitative discipline. • Minimum of 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 the following areas: • 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 Preferred • 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. WHAT'S IN IT FOR YOU: • Advance frontier research in financial AI: Work on challenging research problems at the intersection of deep learning, Transformer-based modeling, time-series foundation models, and quantitative finance, with the opportunity to generate meaningful commercial impact. • Access differentiated data and research resources: Apply modern AI methods to proprietary, high-quality cross-asset market data, supported by the computational resources needed for large-scale model training and empirical research. • Join a rigorous research culture: Be part of a dedicated Applied AI research team that values scientific rigor, strong baselines, robust out-of-sample evaluation, reproducibility, and economically meaningful results. • Collaborate on real market problems: Partner closely with quantitative researchers, strategists, and trading stakeholders across businesses to formulate impactful research questions in forecasting, alpha generation, liquidity, market microstructure, and execution. • Engage with the broader research community: Contribute to thought leadership through internal research forums and, subject to applicable firm policies and approvals, external publications and engagement with the AI and quantitative research community. • Shape the Applied AI research agenda: Develop deep expertise at the intersection of foundation models and financial markets, while helping define the team’s research priorities and future direction Salary Range The expected base salary for this New York, New York, United States-based position is $115,000-$30,0000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end. ABOUT GOLDMAN SACHS: At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: goldmansachs.com

Requirements

Must-haves

Title: Applied AI – Vice President Location: New York, NY Division: Engineering – Applied AI
Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, or a related quantitative discipline.
Minimum of 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 the following areas: • 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.

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Keywords to mirror

machine learningai

Platform: goldman-sachs
Effort: medium

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