below marketmost similar roles pay hereabove market
This role pays less than
53%
of similar roles. Most pay
$173,200–$246,150
— the blue band above.
At the midpoint, this role pays about
$208k
versus about
$210k
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
71 open roles
on FindRole.
Listed pay typically runs
$140,000–$250,000
across 34 roles with salary data.
JOB TITLE: VP Applied AI - Deep Learning
As a Senior Applied AI Research Engineer within the Engineering Applied AI team, you will conduct original research at the frontier of deep learning, time-series modeling, and quantitative finance. You will own the end-to-end research process, including framing questions, engineering data, designing model architectures, and running large-scale training experiments. Your work focuses on developing Transformer-based and time-series foundation models using self-supervised learning and transfer learning. You will apply these methods to solve complex problems in alpha generation, liquidity modeling, market microstructure, and volatility forecasting. Key technical requirements include expertise in representation learning, multivariate forecasting, and high-frequency market data. You will collaborate with trading desks to translate research into economically meaningful, risk-aware quantitative insights while ensuring reproducibility and adherence to responsible AI standards.
Conduct original research on deep learning, time-series modeling, and quantitative finance.
Develop Transformer-based and time-series foundation models using large-scale pre-training and self-supervised learning.
Design rigorous studies of predictive performance, robustness, and economic relevance using walk-forward testing.
Formulate high-value research questions for alpha generation, liquidity modeling, and portfolio construction.
Own the end-to-end research process from data engineering to model architecture and large-scale training.
Create reusable research artifacts including pretrained models, datasets, benchmarks, and evaluation protocols.
Mentor junior researchers and provide technical reviews of research designs and empirical methodologies.
Ensure all research complies with model-risk management, data governance, and responsible AI standards.
What we're looking for
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 deep learning, representation learning, time-series forecasting, 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 (preferred).
Research experience in quantitative trading, systematic investing, market microstructure, electronic execution, market making, statistical arbitrage, or financial econometrics (preferred).
PyTorch
JAX
Transformer
Time-series Foundation Models
Distributed Training
Machine Learning
Data Pipelines
Mixed Precision
model-serving
Optimization
Quantitative Trading
HFT
Electronic Market Making
Statistics
Operations Research
Artificial Intelligence
Generative AI
Data Strategy
Data Modeling
Process Automation
Business Process Re-Engineering
Portfolio Management
Version Control
Model Lifecycle
Data Platforms
Requirements Gathering
User Stories
POC
AI
machine-learning
LLM Gateways
APIs
Distributed Systems
Telemetry
Observability
Kong
MCP
A2A
FinOps
Identity and Authorization
Red Teaming
Drift Detection
Model Lifecycle Management
Policy Engines
Data Architecture
Generative AI
Machine Learning
SQL
RAG
LLM
Data Modeling
Data Integration
APIs
Data Visualization
Predictive Analytics
Automation
Data Lineage
Metrics Definition
Data Transformation