Fully Funded ML Research Fellow for Quant Finance

Trading Interview; Pennsylvania; Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Fully funded fellowship

Collaborative research environment

Access to large datasets

Job summary

Susquehanna is launching a 12-18 month fully funded faculty fellowship to pursue advanced machine learning research in a fast-paced, real-world environment, collaborating with teams at the frontier of quantitative trading.

The role focuses on developing novel ML techniques, leveraging large financial datasets, and translating insights into production-scale systems while growing the research community through collaborations and sabbatical-style engagements.

Qualifications

• Exceptional faculty with a strong ML/academic background.
• Newly minted PhDs or postdocs developing a research agenda.
• Solid theoretical ML foundation with a track record of solving practical problems.
• Strong programming skills in Python; experience with ML frameworks.

Responsibilities

• Conduct applied ML research using large-scale financial datasets.
• Develop novel modeling techniques for quantitative finance.
• Collaborate with researchers and engineers to productionize insights.
• Contribute to robust, high-performance ML infrastructure.
• Explore research directions with flexibility in scope and duration.
• Publish technical research papers in collaboration with hosts.

Skills

Machine learning

Deep learning

LLM

Statistics

Applied mathematics

Physics

Python programming

Education

PhD in ML/CS/Statistics

Postdoctoral fellowship

Tools

PyTorch

TensorFlow

Jax

Job description

Job Type Full-time

Posted July 2025

The role

Job description

Overview

Susquehanna is launching a 12–18 month fully funded faculty fellowship. This is a unique opportunity to pursue advanced machine learning research in a fast-paced, real-world environment - collaborating with teams at the frontier of quantitative trading.

At Susquehanna, our research leverages vast and diverse datasets, applying cutting-edge machine learning at scale to uncover actionable insights - driving data-informed decisions from predictive modeling to strategic execution.

What you'll do

• Conduct applied machine learning research using large-scale, real-world financial datasets
• Develop novel modeling techniques and adapt state-of-the-art algorithms to unique challenges in quantitative finance
• Collaborate with researchers and engineers to translate theoretical insights into production-scale systems
• Contribute to the design of robust, high-performance ML infrastructure
• Explore research directions aligned with your interests, with flexibility in scope and duration
• Evaluate ideas in an industrial setting, generating insights that may inform future academic or applied work
• Help grow our research community by fostering collaboration and leveraging your network within the ML and academic ecosystems

What we're looking for

• Exceptional faculty (tenured or tenure-track) with expertise in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
• Exceptional newly minted PhDs or postdocs developing a research agenda in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
• A strong theoretical foundation in ML and a passion for solving practical, open-ended problems
• Strong programming skills (Python preferred); experience with ML frameworks like PyTorch, TensorFlow or Jax
• Intellectual curiosity, adaptability, and a collaborative mindset

Note: This fellowship is ideal for faculty seeking to broaden their applied research portfolio, explore new domains, or engage in sabbatical collaborations. The faculty fellowship is also appropriate for exceptional newly minted PhD and postdocs who want to develop a research agenda (involving, but not limited to, modeling, inference, and prediction tasks in complex systems), as they prepare to transition into a faculty position. While research outputs cannot be published due to the proprietary nature of our work, we aim for each faculty fellow to publish technical research papers collaboratively with their research hosts, to showcase some of the machine learning and AI innovations that they developed while in residence at Susquehanna.

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

If you're a recruiting agency and want to partner with us, please reach out to [e-mail]. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

#LI-MP1

#LI-Onsite

• Exceptional faculty (tenured or tenure-track) with expertise in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
• Exceptional newly minted PhDs or postdocs developing a research agenda in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
• A strong theoretical foundation in ML and a passion for solving practical, open-ended problems
• Strong programming skills (Python preferred); experience with ML frameworks like PyTorch, TensorFlow or Jax
• Intellectual curiosity, adaptability, and a collaborative mindset

Note: This fellowship is ideal for faculty seeking to broaden their applied research portfolio, explore new domains, or engage in sabbatical collaborations. The faculty fellowship is also appropriate for exceptional newly minted PhD and postdocs who want to develop a research agenda (involving, but not limited to, modeling, inference, and prediction tasks in complex systems), as they prepare to transition into a faculty position. While research outputs cannot be published due to the proprietary nature of our work, we aim for each faculty fellow to publish technical research papers collaboratively with their research hosts, to showcase some of the machine learning and AI innovations that they developed while in residence at Susquehanna.

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

If you're a recruiting agency and want to partner with us, please reach out to [e-mail]. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

#LI-MP1

#LI-Onsite

This description was published by Susquehanna International Group (SIG). Applications are handled on their careers site: we are not the employer and cannot process them.

Next step

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