ML Research Engineer Intern: Build Scalable Models

G-Research; Greater London; On-site; GBP 78,000 - 112,000; Full time

Benefits offered by this job

Highly competitive compensation plus 1

Lunch provided (via Just Eat for Biz)

30 days’ annual leave

9% company pension contributions

Informal dress code and work/life bal.

Healthcare and life assurance

Cycle-to-work scheme

Monthly company events

Job summary

G-Research in London is offering a 12-week summer internship for exceptional machine learning students to work with world-class researchers on cutting-edge problems. As part of the Technical Machine Learning team you will implement ideas from research papers, write custom libraries to train models on petabytes of data, and help optimise training times while profiling architectures.

The role emphasizes collaboration, independent work, and exposure to modern ML techniques; finance experience is

Qualifications

• Undergraduate, master’s or PhD student in ML or related discipline.
• Proficient in Python with PyTorch and NumPy.
• Experience in optimisation methods, modern ML techniques, HPC, profiling or model inference.
• Ability to develop models when standard approaches fail.
• Strong communication and collaboration skills.
• Finance experience not required; non-financial backgrounds encouraged.

Responsibilities

• Implement ideas from published research papers.
• Write custom libraries to train models on petabytes of data.
• Reduce model training times by hand optimising ML operations.
• Profile custom ML architectures to identify performance bottlenecks.
• Evaluate the latest hardware and software in the ML ecosystem.

Skills

Python

PyTorch

NumPy

OO programming

ML reasoning

Education

Undergraduate/Master’s/PhD student in ML

Job description

G-Research in London is offering a 12-week summer internship for exceptional machine learning students to work with world-class researchers on cutting-edge problems. As part of the Technical Machine Learning team you will implement ideas from research papers, write custom libraries to train models on petabytes of data, and help optimise training times while profiling architectures.

The role emphasizes collaboration, independent work, and exposure to modern ML techniques; finance experience is