The Goldman Sachs Group, Inc

VP AI Research/Applied AI - Deep Learning & Time Series

The Goldman Sachs Group, Inc • $150K — $300K *
New York, NY 10025In-Person
Finance & Insurance
5 - 7 years of experience
Today
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Job Overview by Ladders

Qualifications

  • Bachelor's, Master's, or Ph.D. in relevant quantitative discipline
  • 7+ years in deep learning model development, especially in time series
  • Expertise in modern deep learning architectures and classical econometric techniques
  • Experience with time series architectures and distributed training
  • Strong Python and deep learning framework proficiency (PyTorch, TensorFlow)
  • Solid background in statistics, probability, and optimization
  • Excellent communication skills for conveying complex ideas to diverse audiences

Responsibilities

  • Lead end-to-end deep learning model design and training for financial applications
  • Develop and evaluate advanced architectures like GANs and Transformers
  • Conduct large-scale training leveraging multi-node GPU resources
  • Create robust evaluation frameworks tailored for financial time series
  • Collaborate with quant teams on alpha research and signal generation
  • Contribute to a shared research platform for cross-desk usability
  • Mentor junior researchers and ensure adherence to AI governance standards

Benefits

  • Work on impactful research problems with immediate commercial implications
  • Access to high-quality financial datasets and GPU compute resources
  • Engagement in a rigor-focused standalone research group
  • Opportunities to publish in the broader research community
  • Collaboration with quantitative researchers across multiple areas
  • Develop expertise in deep learning and quantitative finance
Full Job Description
Job Description

THE ROLE:

Title: AI Research - Vice President Location: New York, NY Division: Engineering - AI Research

We are seeking a deeply hands-on researcher to lead the design, training, and evaluation of deep learning models for financial time series. This is an individual contributor role for someone who is equally comfortable deriving a likelihood, writing a distributed training loop across a multi-node GPU cluster, and defending an evaluation methodology to a room of quantitative researchers.

You will own research 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 with quant and engineering teams to bring models into production. Because our output serves multiple desks and asset classes, you will be expected to build models and abstractions that generalize - not one-off solutions.

This is a fast-moving research space. You thrive in ambiguity, you are skeptical of results that look too good, and you bring the same rigor to evaluation methodology that you bring to model design.

WHAT YOU WILL BE WORKING ON:
  • Model research and development: Design, implement, and train modern deep learning architectures for forecasting, representation learning, and generative modelling of financial time series - including CNNs and temporal convolutional networks, Transformers and attention-based sequence models, autoencoders, GANs, diffusion models, graph neural networks, Bayesian networks, and reinforcement learning.
  • Time series specialization: Build and benchmark against specialized sequence architectures including WaveNet, N-BEATS / N-HiTS, DeepAR, PatchTST, and Time Series Foundation Models (TSFMs), and rigorously baseline them against classical econometric methods such as ARIMA, GARCH, Kalman filters, and state space models.
  • Training at scale: Own large-scale model training across the firm's GPU clusters and cloud compute environment - distributed data parallel (DDP), fully sharded data parallel (FSDP), mixed precision, hyperparameter search, and experiment tracking. Optimize inference through quantization, distillation, and ONNX-based deployment paths.
  • Evaluation and validation: Build robust evaluation frameworks tailored to financial data - walk-forward and purged cross-validation, embargo periods, regime-conditional analysis, uncertainty quantification and calibration, ablations, and significance testing that properly accounts for multiple hypothesis testing and data snooping.
  • Applied quantitative research: Partner with quantitative researchers and strategists across desks on alpha research, signal generation, portfolio optimization, and backtesting, translating model outputs into economically meaningful, risk-adjusted, capacity-aware signals.
  • Firmwide research platform: Contribute reusable models, datasets, benchmarks, and tooling to a shared research platform that serves multiple desks and asset classes, raising the quality and reproducibility bar across the firm.
  • Technical leadership: Mentor junior researchers and engineers, review research designs and code, and present findings to senior technical and business stakeholders.
  • AI Governance: Ensure all models adhere to the firm's model risk management, data privacy, ethics, and safety standards, with full documentation, lineage, and auditability.

SKILLS AND EXPERIENCE WE ARE LOOKING FOR:

Required
  • A Bachelor's, Master's, or Ph.D. degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, Quantitative Finance, or a related quantitative discipline.
  • A minimum of 7 years of industry experience building, training, and deploying deep learning models, with a substantial portion focused on sequential or time series data. Candidates with a Ph.D. and fewer years of industry experience will be considered where the depth of research experience is demonstrably equivalent.
  • Deep expertise across modern deep learning architectures: CNNs and TCNs, Transformers, autoencoders, GANs, diffusion models, GNNs, Bayesian methods, and reinforcement learning.
  • Strong working knowledge of classical time series and econometric modelling - ARIMA, GARCH, Kalman filtering, state space models - and clear judgment on when deep learning does and does not beat them.
  • Practical experience with modern time series architectures such as WaveNet, N-BEATS / N-HiTS, DeepAR, PatchTST, or Time Series Foundation Models.
  • Expert-level Python and deep proficiency in PyTorch, TensorFlow / Keras and/or JAX / Flax.
  • Demonstrated experience with distributed and accelerated training (DDP, FSDP, multi-node GPU training) and model export and optimization workflows including ONNX.
  • Rigorous foundations in statistics, probability, stochastic processes, optimization, and signal processing.
  • Excellent oral and written communication skills, with the ability to articulate research trade-offs to both PhD researchers and non-technical business stakeholders.
  • Comfort operating in a quickly evolving environment with a high degree of ambiguity and rapid change.

Preferred
  • Publications at top-tier venues such as NeurIPS, ICML, ICLR, AISTATS, or KDD, or in leading quantitative finance journals.
  • Prior machine learning or data science experience at a hedge fund, asset manager, proprietary trading firm, or systematic trading desk.
  • Experience across multiple asset classes - equities, fixed income, FX, commodities, or multi-asset.
  • Familiarity with Bayesian deep learning, probabilistic programming, or conformal prediction for uncertainty quantification.
  • Experience with cloud platforms and containerized training environments (AWS, Kubernetes, Docker).
  • Exposure to model risk management or regulatory frameworks in financial services.
  • Contributions to open-source machine learning or time series libraries.

WHAT'S IN IT FOR YOU:
  • Work on genuinely hard research problems with immediate, measurable commercial impact across the firm.
  • Access to proprietary, high-quality cross-asset financial datasets at a scale few institutions can offer, and GPU compute to model them properly.
  • Join a standalone research group that values rigor - strong baselines, rigorous evaluation, and reproducibility.
  • Freedom to publish and engage with the broader research community.
  • Direct partnership with quantitative researchers and strategists across multiple desks who will use what you build.
  • Develop deep expertise at the intersection of frontier deep learning and quantitative finance, with the scope to shape the firm's applied AI research agenda.


Salary Range
The expected base salary for this New York, New York, United States-based position is $150,000-$300,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.

About The Goldman Sachs Group, Inc

The Goldman Sachs Group, Inc. provides investment banking, securities, and investment management services, as well as financial services to corporations, financial institutions, governments, and high-net-worth individuals worldwide. Its Investment Banking segment offers financial advisory services, including advisory assignments concerning mergers and acquisitions, divestitures, corporate defense, risk management, and restructurings and spin-offs; and underwriting services comprising public offerings and private placements of a range of securities, loans, and other financial instruments, and derivative transactions. The company’s Institutional Client Services segment provides client execution services, such as fixed income, currency, and commodities client execution related to making markets in interest rate products, credit products, mortgages, currencies, and commodities; and equities related to making markets in equity products, as well as executes and clears institutional client transactions on stock, options, and futures exchanges. This segment also engages in the securities services business providing financing, securities lending, and other brokerage services to institutional clients, including hedge funds, mutual funds, pension funds, and foundations. Its Investing and Lending segment originates longer-term loans; and invests in debt securities, loans, public and private equity securities, real estate, consolidated investment entities, distressed assets, currencies, commodities, and power generation facilities. The company’s investment management segment provides investment products and services, as well as offers wealth advisory services, including portfolio management and financial counseling, and brokerage and other transaction services.

The Goldman Sachs Group, Inc. Careers

Join the prestigious team at The Goldman Sachs Group, Inc., a global leader in finance and investments, and propel your career to new heights. Our firm is renowned for its commitment to excellence, innovation, and leadership in the financial sector.

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Goldman Sachs is not just a workplace. It is a community where you can build lasting relationships with colleagues and industry leaders. Our networking events, mentorship programs, and professional groups foster connections that can accelerate your career trajectory.

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At The Goldman Sachs Group, Inc., your career is just the beginning – it’s an opportunity to excel and lead in the global financial industry. Join us and make your mark!
Learn more about The Goldman Sachs Group, Inc
Size
45,100 employees
Market Cap
$115.8 billion
Industry
Finance & Insurance
Net Income
$9.4 billion
Founded
1869
5 Year Trend
+11.3%
Revenue
$53.4 billion
NASDAQ
GS
* Ladders Estimates

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