Machine Learning Researcher (Foundational Models)

Fintal Partners; New York (NY); On-site; USD 180,000 - 320,000; Full time

Job summary

Fintal Partners in New York hires proven quantitative researchers to apply ML and DL to trading challenges. You will join a central ML research group collaborating with trading desks to build models, systems and tooling, blending ML with trading intuition and problem-solving.

Expect deep involvement in feature engineering, alpha research, and deploying predictive models across asset classes. You’ll work with researchers and engineers to turn market insights into data-driven features, with

Qualifications

• PhD or Master's in Engineering, Math, Statistics, CS or a comparable quantitative discipline.
• 2+ years building applied ML models; trading environment experience preferred.
• Demonstrated skill developing and deploying predictive foundational models.
• Strong Python, with ML libraries such as PyTorch or TensorFlow and/or high-performance frameworks like JAX.
• Solid grasp of the theory behind state-of-the-art ML models.
• Strong publication record at ICML, ICLR, NeurIPS or equivalent.
• Genuinely collaborative, with excellent written and verbal communication.

Responsibilities

• Build and deploy machine learning models that lift trading performance across asset classes.
• Research, test and prototype new algorithmic ideas, applying advanced ML to market prediction, signal generation and portfolio optimization.
• Work with quantitative traders, researchers and developers to turn market insight into data-driven features and models.
• Own data acquisition, preprocessing and feature engineering across structured and unstructured sources.

Skills

Python programming

Machine learning

Research collaboration

Communication

Education

PhD or Master's in Engineering, Math, Statistics, CS

Tools

PyTorch

TensorFlow

JAX

Job description

A leading proprietary trading firm is hiring proven quantitative researchers to apply cutting-edge machine learning and deep learning to hard trading problems. The role sits within a central ML research group that partners with trading desks across the firm. You'll be working closely with other researchers and engineers to build and steadily improve models, systems and research tooling, the belief here is that research-led work succeeds when ML and statistics skills sit alongside trading intuition, pragmatism and a problem-solving instinct. Expect deep involvement in feature engineering and alpha research, applying a broad range of ML models and building custom ones.

What you'll be doing

• Build and deploy machine learning models that lift trading performance across asset classes
• Research, test and prototype new algorithmic ideas, applying advanced ML to market prediction, signal generation and portfolio optimization
• Work with quantitative traders, researchers and developers to turn market insight into data-driven features and models
• Own data acquisition, preprocessing and feature engineering across structured and unstructured sources

What we're looking for

• PhD or Master's in Engineering, Math, Statistics, Computer Science or a comparable quantitative discipline
• 2+ years building applied ML models; trading environment experience preferred
• Demonstrated skill developing and deploying predictive foundational models
• Strong Python, with ML libraries such as PyTorch or TensorFlow and/or high-performance frameworks like JAX
• Solid grasp of the theory behind state-of-the-art ML models
• Strong publication record at ICML, ICLR, NeurIPS or equivalent
• Genuinely collaborative, with excellent written and verbal communication

About the firm

A research-led trading firm where quantitative modelling, machine learning and engineering shape how markets are traded. A steadying presence for decades, it provides liquidity across trading venues with a focus on value and risk management for investors. Using its own capital and technology, the firm builds proprietary systems and algorithms operating in markets worldwide, with researchers, traders and engineers working as one group.