AI Research Associate: Time Series & Deep Learning

SRI Conference; New York (NY); On-site; USD 100,000 - 200,000; Full time

Job summary

Goldman Sachs seeks an AI Research Associate in New York to lead design, training, and evaluation of deep learning models for financial time series. You will own end-to-end research, from data curation to production, and collaborate with quantitative researchers to translate findings into robust, risk-aware signals.

The role demands hands-on building of architectures (CNNs, Transformers, GNNs), distributed training, and strong evaluation discipline in a fast-moving, governance-driven environment.

Qualifications

• Bachelor's/Master's/PhD in Computer Science, ML, or related quantitative field.
• 3+ years of industry experience building and deploying deep learning models, especially sequential/time-series.
• Expertise in modern DL architectures and time-series modelling.
• Strong Python and ML framework proficiency.
• Experience with distributed multi-node GPU training and model deployment.

Responsibilities

• Design, train, and evaluate deep learning models for financial time series.
• Lead end-to-end research from data curation to productionisation.
• Build robust evaluation frameworks and rigorous baselines.
• Collaborate with quantitative researchers to translate findings into signals.
• Mentor junior team members and contribute to governance and documentation.

Skills

Deep learning

Time series

PyTorch

TensorFlow

Distributed training

Python

Education

CS/ML degree

Tools

PyTorch

TensorFlow

Keras

JAX

ONNX

Job description

Goldman Sachs seeks an AI Research Associate in New York to lead design, training, and evaluation of deep learning models for financial time series. You will own end-to-end research, from data curation to production, and collaborate with quantitative researchers to translate findings into robust, risk-aware signals.

The role demands hands-on building of architectures (CNNs, Transformers, GNNs), distributed training, and strong evaluation discipline in a fast-moving, governance-driven environment.