below marketmost similar roles pay hereabove market
This role pays more than
99%
of similar roles. Most pay
$162,000–$235,750
— the blue band above.
At the midpoint, this role pays about
$1050k
versus about
$199k
for comparable roles.
Based on 240 similar postings.
Employer
About Goldman Sachs
Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.
Goldman Sachs currently has
70 open roles
on FindRole.
Listed pay typically runs
$140,000–$250,000
across 34 roles with salary data.
JOB TITLE: Associate Applied AI - Deep Learning/Time Series
The AI Research group seeks an Associate to lead the design, training, and evaluation of deep learning models for financial time series. This individual contributor role involves owning research problems end-to-end, including curating data, designing architectures, and running large-scale training experiments across multi-node GPU clusters. You will build models for forecasting, representation learning, and generative modeling, specifically utilizing CNNs, Transformers, GANs, diffusion models, and reinforcement learning. You must benchmark these against classical econometric methods like ARIMA and GARCH. Key technologies include Python, PyTorch, TensorFlow, JAX, and ONNX. The role solves the challenge of advancing sequence modeling and probabilistic forecasting for noisy, non-stationary financial data, delivering a firmwide platform for quantitative researchers to generate risk-adjusted signals across various asset classes.
Design, implement, and train deep learning architectures for forecasting and generative modeling of financial time series.
Build and benchmark specialized sequence architectures against classical econometric methods like ARIMA and GARCH.
Manage large-scale model training across multi-node GPU clusters using distributed data parallel and mixed precision techniques.
Develop robust evaluation frameworks including walk-forward cross-validation, uncertainty quantification, and significance testing.
Partner with quantitative researchers to translate model outputs into risk-adjusted, capacity-aware signals for alpha research.
Contribute reusable models, datasets, and tooling to a shared firmwide research platform.
Mentor junior researchers and review research designs and code for technical accuracy.
Ensure all models comply with firm standards for risk management, data privacy, and auditability.
What we're looking for
Hold 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.
Have at least 3 years of industry experience building, training, and deploying deep learning models, specifically focused on sequential or time series data.
Possess deep expertise in modern deep learning architectures including CNNs, Transformers, autoencoders, GANs, diffusion models, GNNs, Bayesian methods, and reinforcement learning.
Demonstrate strong working knowledge of classical time series and econometric modeling such as ARIMA, GARCH, Kalman filtering, and state space models.
Have practical experience with modern time series architectures like WaveNet, N-BEATS / N-HiTS, DeepAR, PatchTST, or Time Series Foundation Models.
Possess expert-level Python skills and deep proficiency in PyTorch, TensorFlow / Keras, and/or JAX / Flax.
Demonstrate experience with distributed and accelerated training (DDP, FSDP, multi-node GPU training) and model export/optimization workflows including ONNX.
Maintain rigorous foundations in statistics, probability, stochastic processes, optimization, and signal processing.
Python
PyTorch
TensorFlow
Keras
HuggingFace Transformers
Reinforcement Learning
LLMs
Multi-Agent Systems
Synthetic Data Generation
Foundation Model Training
LangGraph
Coq
Isabelle
Lean
TLA+
Alloy
Z3
PDDL
Statistical Data Analysis
Generative AI
Python
PyTorch
LangChain
AWS
Spark
Vector Databases
RAG
Multi-Agent Systems
Deep Learning
NLP
SQL
Scala
R
Machine Learning
Statistics
Time Series
Clustering
Sentiment Analysis
Generative AI
Machine Learning
Python
Large Language Models
PyTorch
scikit-learn
Hugging Face
LangChain
AWS
Spark
Agile
Data Structures
Algorithms
Deep Learning
Transformers
Prompt Engineering
Knowledge Graphs
Computer Vision
Reinforcement Learning
Time Series Analysis