Campus AI Research Engineer (Research Automation) (Intern)

Location: Chicago; New York

Salary: $300,000 per year (estimated base salary, annualized)

Company

hirify.global Group builds and deploys technologies that support global financial markets research and trading.

What you will do

• Apply state-of-the-art techniques to complex and challenging domains.
• Collaborate with researchers and quants to build flexible, reusable frameworks for financial ML.
• Optimize ML training pipelines to make best use of HPC resources.
• Integrate ML models into production systems where latency matters.
• Build large-scale ML systems that are observable, performant, and flexible, reducing research iteration cycle time.
• Work across C/C++, Python, CUDA, and other low-level GPU languages.

Requirements

• Strong publication record (ICML, ICLR, AAAI, NeurIPS, UAI, KDD) and/or contributions to open-source AI research.
• Strong general ML background with exposure to modern deep learning and/or language modeling architectures (e.g., transformers, SSMs).
• Solid development skills in Python and/or C++.
• Familiarity with ML libraries/frameworks such as PyTorch, JAX, and/or TensorFlow.
• Ability to reason through quantitative problems and communicate effectively with trading researchers.
• Reliable and predictable availability.

Nice to have

• Experience with HPC and distributed large model training.
• GPU performance optimization experience (CUDA or ROCm).
• Experience with end-to-end model development.
• Strong opinions on ML research best practices, tooling, and/or infrastructure.

Culture & Benefits

• Research-driven environment where outcomes inform trading and technology development.
• Collaboration with researchers, quants, and engineers to build and deploy ML systems.
• Work with HPC resources and production systems where performance and latency matter.
• International students encouraged to apply; CPT/OPT eligibility considered and work visas sponsored for full-time positions.

Hiring process

• Application review based on research record and technical fit.
• Interviews to assess ML/research-to-production capabilities and collaboration skills.
• Final evaluation of availability and eligibility for the internship/visa pathway.