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About the Role
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.
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Requirements
- 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.
- 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.
Skills
- Deep Learning
- Transformers
- Time-Series Foundation Models
- Representation Learning
- Generative Modeling
- Pre-training
- Self-supervised Learning
- Transfer Learning
- Adaptation
- Walk-forward testing
- Leakage controls
- Regime analysis
- Statistical validation
- Alpha generation
- Return forecasting
- Volatility forecasting
- Liquidity modeling
- Market microstructure
- Execution
- Portfolio construction
- Quantitative research
- Model risk management
- Data governance
- Privacy
- Ethics
- Responsible AI
- Probabilistic deep learning
- Sequence architectures
- Multivariate forecasting
- Cross-sectional prediction
- High-frequency market data
- Quantitative trading
- Systematic investing
- Electronic execution
- Market making
- Statistical arbitrage
- Financial econometrics
- Machine learning
- Artificial intelligence
- Statistics
- Optimization
- Quantitative finance
Location
- New York, NY
Work Type
- Full-time
Experience Level
- Vice President
- Senior
- 5+ years
Education Level
- Ph.D.
Salary/Compensations
- $115,000-$300,000
Benefits
- Discretionary bonus
About the Company
- 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.
Equal Opportunity
- We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.
- Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html