Machine Learning Researcher

Fintal Partners; Chicago (IL); On-site; USD 120,000 - 180,000; Full time

Job summary

Fintal Partners is seeking a Machine Learning Researcher to design and deploy cutting-edge deep learning models for complex financial market prediction problems. You will work within a central ML research team and collaborate with quantitative researchers, traders, and engineers to translate insights into live trading features and strategies.

The role emphasizes taking ideas from theory through experimentation to production, with opportunities to influence real-world trading decisions in a

Qualifications

• PhD in Computer Science, Engineering, Mathematics, Statistics or another highly quantitative discipline.
• 2+ years of experience building and deploying advanced machine learning models.
• Strong programming skills in Python with hands-on experience using PyTorch, JAX, TensorFlow, or similar modern ML frameworks.
• Strong research background with publications at leading conferences such as NeurIPS, ICML, ICLR, or equivalent venues.

Responsibilities

• Research, design, and deploy advanced machine learning and deep learning models for challenging prediction problems.
• Research new algorithmic ideas and explore advanced machine learning techniques applicable to market prediction, signal generation, and portfolio optimization.
• Apply state-of-the-art machine learning research to large, complex structured and unstructured datasets.
• Collaborate with quantitative researchers and traders to translate market insights into predictive features and models.
• Contribute to a highly collaborative research environment focused on continuously advancing the firm's machine learning capabilities.

Skills

Python

Research

Publications

Education

PhD in Computer Science, Engineering, Mathematics, Statistics

Tools

PyTorch

JAX

TensorFlow

Job description

A leading quantitative trading firm is hiring a Machine Learning Researcher to develop cutting-edge deep learning models for complex prediction problems in financial markets. This role is ideal for researchers with deep expertise in modern machine learning architectures who enjoy taking ideas from theoretical research through experimentation and into production. You'll work within a central machine learning research team and collaborate closely with quantitative researchers, traders, and engineers to develop models that directly influence live trading strategies.

What You'll Be Doing

• Research, design, and deploy advanced machine learning and deep learning models for challenging prediction problems.
• Research new algorithmic ideas and explore advanced machine learning techniques applicable to market prediction, signal generation, and portfolio optimization.
• Apply state-of-the-art machine learning research to large, complex structured and unstructured datasets.
• Collaborate with quantitative researchers and traders to translate market insights into predictive features and models.
• Contribute to a highly collaborative research environment focused on continuously advancing the firm's machine learning capabilities.

What We're Looking For

• PhD in Computer Science, Engineering, Mathematics, Statistics, or another highly quantitative discipline.
• 2+ years of experience building and deploying advanced machine learning models.
• Strong programming skills in Python with hands-on experience using PyTorch, JAX, TensorFlow, or similar modern ML frameworks.
• Strong research background with publications at leading conferences such as NeurIPS, ICML, ICLR, or equivalent venues.
• Experience within quantitative trading is valuable but not required.

Why This Opportunity?

• Greenfield opportunity to conduct cutting-edge machine learning research where successful models can directly influence real-world trading decisions.
• Work on challenging modeling problems involving highly dynamic financial datasets.
• Collaborate directly with leading quantitative researchers, traders, and engineers in a research-driven environment.
• Build novel modeling approaches rather than being limited to applying existing off-the-shelf solutions.
• Join a central ML research function with the resources and technical environment to pursue ambitious machine learning problems.