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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Associate Applied AI - Deep Learning/Time Series - **Company:** The Goldman Sachs Group Inc - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $100,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Computer Clusters, Distributed Data Store, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Signal Processing, Reinforcement Learning, Graph Neural Networks, Pytorch, Uncertainty Quantification (UQ), Large Language Models, Deep Learning, Generative AI, Keras, Discretization, Kubernetes, Information Technology, AISTATS, ONNX (Open Neural Network Exchange) Format, Mixed Precision, Feature Extraction, Docker - **Published:** October 21, 2026 - **Apply:** https://diversityjobs.com/career/18694081/Associate-Applied-Ai-Deep-Learning-Time-Series-New-York-New-York ## About the Role * 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 3+ 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. ## Description 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.