21 hours ago
London, United KingdomIntern
Responsibilities
- Implement ideas from published machine learning research papers.
- Write custom libraries to efficiently train models on petabytes of data.
- Optimize machine learning operations to reduce model training times.
- Profile custom machine learning architectures to identify performance bottlenecks.
- Evaluate current hardware and software in the machine learning ecosystem.
- Develop models when standard approaches are insufficient while working independently and collaboratively.
Requirements
- Be a current undergraduate, master’s, or PhD student in machine learning or a related discipline.
- Have strong object-oriented programming skills.
- Experience with Python, PyTorch, and NumPy is desirable.
- Experience in one or more of advanced optimization methods, modern machine learning techniques, HPC, profiling, or model inference is desirable.
- Demonstrate strong machine learning reasoning skills.
- Communicate and collaborate effectively in teams with complementary expertise.
- Finance experience is not required.
Benefits
- 12-week summer internship from 28 June 2027 to 17 September 2027.
- Working hours are 09:00–17:30.
- Highly competitive compensation plus an annual discretionary bonus.
- Lunch provided via Just Eat for Business and access to a dedicated barista bar.
- 30 days of annual leave.
- 9% company pension contributions.
- Informal dress code and work/life balance.
- Comprehensive healthcare and life assurance.
- Cycle-to-work scheme.
- Monthly company events.
Categories
About G-Re
G-Research is a London-based quantitative research and technology firm that develops models, data pipelines, and trading systems to forecast movements in global financial markets. It employs researchers and software engineers to build machine-learning-driven strategies and the infrastructure that supports systematic investing, spanning high-performance compute and secure networks. Privately held and founded in 2001, the company operates from its London headquarters and focuses on institutional, data-intensive finance.