Machine Learning Research Engineer Internship - 2027 [New York]
Quantbot Technologies·New York·United States·Hedge Fund & Quant
Quantbot Technologies is hiring a Machine Learning Research Engineer Internship - 2027 [New York] in New York. Posted 2026-09-04; applications close 2026-11-03 (in 45 days).
Role details
About Quantbot Technologies
Quantbot Technologies is a leading global quantitative investment firm deploying systematic strategies across asset classes. Our approach is powered by large-scale research supported by advanced computing infrastructure and comprehensive market and historical data. We combine rigorous scientific methods with modern statistical and machine learning techniques to discover, test, and implement novel investment ideas.
Machine Learning Research Engineering Intern
As a Machine Learning Research Engineering Intern, you will design and evaluate machine learning systems that support quantitative research. You will work alongside experienced researchers and engineers to build scalable pipelines, process large datasets, and apply state-of-the-art machine learning methods to real-world problems in systematic investing. This internship provides exposure to both practical engineering challenges and research-driven exploration in machine learning for quantitative finance.
Who Should Apply
- Ideal candidates are students pursuing an advanced degree who are curious, creative, and eager to learn. You enjoy tackling challenging problems at the intersection of software and machine learning. No prior finance experience is required, though an interest in quantitative finance is a plus.
- You should be comfortable coding in at least one major language such as Python, Rust, or C/C++, and familiar with modern ML frameworks like PyTorch, TensorFlow, or JAX. We are looking for strong engineering skills—writing clean, efficient code and working hands-on with model development and deployment while contributing effectively to the team.
- We prioritize curiosity, talent, and impact over simply checking boxes. You will thrive if you can balance independent work with collaboration, communicate clearly, manage your time effectively, and remain open to learning from mentors and peers.
- Bonus points for staying current with advances in AI/ML, experience optimizing algorithms for specialized hardware, or contributing to open-source projects.
Program Details
- This is a 10-week program running from June to August.
- Training and support are provided, including a dedicated mentor.
- Successful interns may receive full-time job offers.
Our Culture
Join a global team that values a relaxed, collaborative environment. Enjoy company outings, traditional Friday lunches, and a focus on work-life balance. Quantbot is committed to fostering an inclusive workplace where all employees can thrive.
Equal Opportunity Employer
Quantbot is an Equal Opportunity Employer and does not discriminate based on race, color, religion, national origin, sex or sexual orientation, physical or mental disability, or age.
Compensation
The annual base pay for this role is expected to be between $180,000 and $200,000, prorated based on the internship start and end date. The expected range is based on information at the time this post was generated. Actual compensation for the successful candidate will be determined based on factors such as skills, qualifications, experience, and level of education.
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Applying to this role
This Machine Learning Research Engineer Internship - 2027 [New York] role at Quantbot Technologies runs through the firm's own careers portal and expects a CV and cover letter written specifically for the posting, not a portable submission carried across firms. Jorb AI's application agent tailors a CV and cover letter from your background to this posting and tracks the role alongside the rest of your applications.
Jorb AI tracks details for Machine Learning Research Engineer Internship - 2027 [New York] at Quantbot Technologies. Postings refresh hourly from primary careers pages. Job details mirror the firm's posting; the apply link goes directly to the source. Last refreshed 2026-09-19.
