Artificial Intelligence Researcher

Goliath Partners; New York (NY); On-site; USD 400,000 - 2,000,000; Full time

Job summary

Goliath Partners is seeking an ML researcher to advance alpha generation using deep learning, NLP, reinforcement learning, and large-scale ensemble methods. You will design, train, and deploy models across equities and futures, owning the end-to-end pipeline from research to live production.

Ideal candidates have 2–5 years of ML experience in quantitative environments, strong statistics, and Python proficiency. A PhD or equivalent industry experience is highly valued.

Qualifications

• 2 to 5 years of experience applying machine learning in quantitative research or trading environments.
• Strong foundations in statistics, ML, and time-series/panel data methods.
• Proficiency in Python; experience with large-scale data pipelines and deployment infrastructure.
• PhD or 2 years of industry experience in ML, statistics, CS, or related field is a strong plus.

Responsibilities

• Design, train, and deploy ML models for alpha generation across equities, futures, and other liquid markets.
• Own the full pipeline from research and backtesting to live production deployment.
• Mine large, noisy datasets to engineer robust features and signals.
• Collaborate with quants, engineers, and traders to integrate ML signals into existing strategies.

Skills

Python

Time-series analysis

Statistics

Machine learning

Education

PhD in ML/Statistics/CS

Quantitative degree or related field

Tools

TensorFlow

PyTorch

Scikit-learn

Job description

Top-tier systematic trading firm hiring an ML researcher to push the firm's alpha research beyond classical statistical methods.

Compensation

• Base: $400K
• Total Comp: $1M-2M
• Structure: Base + Sign-on Bonus + P&L attachment + performance bonus

Role Description

• Design, train, and deploy ML models (deep learning, NLP, reinforcement learning, or large-scale ensemble methods) for alpha generation across equities, futures, and other liquid markets
• Own the full pipeline from research and backtesting to live production deployment
• Mine large, noisy, non-stationary financial datasets to engineer features and signals that hold up out-of-sample
• Collaborate with quant researchers, engineers, and traders to integrate ML-driven signals into existing systematic strategies

Ideal Candidate

• 2 to 5 years of experience applying machine learning in a quantitative research, trading, or comparable high-stakes production environment
• Strong foundations in statistics, applied ML, and time-series/panel data methods; healthy skepticism about overfitting in noisy financial data
• Proficiency in Python; experience with large-scale data pipelines and model deployment infrastructure
• PhD or 2 years of industry experience in ML, statistics, CS, or a related quantitative field a strong plus