Tower Research Capital is a leading quantitative trading firm founded in 1998. Tower has built its business on a high-performance platform and independent trading teams. We have a 25+ year track record of innovation and a reputation for discovering unique market opportunities.

Tower is home to some of the world’s best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization. 

Engineers thrive at Tower while developing electronic trading infrastructure at a world class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability and performance.

At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do — combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best.

At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential.

Responsibilities:

• Developing data pipelines for cleaning, collecting, processing, and analyzing diverse datasets.
• Designing and implement machine learning and LLM-based NLP models.
• Conducting experiments and tests to evaluate model performance.
• Contributing to optimizing and fine-tuning existing machine learning models.

Qualifications:

• A bachelor’s, master's, or PhD (ongoing or complete) degree or equivalent from a top university, available to join for an in-office 6 month Internship starting Jan 2025/Feb 2025.
• Prior experience with training, building, and deploying models via Tensorflow/Pytorch (or similar) is mandatory.
• Experience with CI/CD pipelines and MLOps for automating model deployments.
• Skilled in using Linux, SQL, Git, and BASH scripting.
• Strong knowledge of Python and hands-on.