How do I apply for the AI Engineering Intern position at G-Research and track my application?

We provide a direct link to the official application portal. You can apply directly here . Additionally, you can easily track this and other applications by clicking the bookmark icon to save it to your YGI Kanban board .

Is the internship at G-Research paid and what compensation can candidates expect?

Yes, this is a paid internship opportunity. Exact compensation details are typically finalized during the company interview process.

What are the working arrangements and expected duration for the AI Engineering Intern role?

This position is a full-time, structured as a on-site role.

What educational background or degree level is required for the AI Engineering Intern at G-Research?

Candidates are generally expected to be holding or pursuing the following degree levels: Bachelor.

Does G-Research provide visa sponsorship or assistance for international applicants?

Regarding visa assistance and sponsorship for this role, the official status is currently listed as: Not disclosed.

AI Engineering Intern

G-Research

Found 2 months ago

Location

London, United Kingdom

Time

Full-time

Work Mode

On-site

Salary

Highly competitive compensation plus annual discretionary bonus

Visa Help

Not disclosed

Last Verified

2 months ago

Education

Bachelor

Skills & Qualifications

Technical Skills

C#
Python
distributed systems
LangGraph
LangChain
Pydantic
FastAPI
MCP
RAG pipelines
Agentic workflows
GPU scheduling
model-parallel inference
vLLM
TensorRT-LLM

Soft Skills

complex, systems-level challenges
developer experience
platform ergonomics
owning services end-to-end
testing
version control
CI/CD
observability
collaboration

Job Description

We’re seeking a Software Engineering Intern to join our Core AI sub-team within the AI Engineering Group at G-Research. The Core AI team builds and evolves the foundational platforms behind every Generative AI initiative within the firm, from RAG services to tooling that improves developer experience across quant and engineering teams. As part of this team, you’ll contribute to the design and delivery of scalable, reliable and secure infrastructure that enables researchers, data scientists and engineers to experiment and deploy AI solutions safely and at speed. Your work will span critical projects and workstreams, including distributed systems development, LLM orchestration and inference, RAG service integration and leading the drive for internal adoption of third-party AI technologies

Requirements

A degree in Computer Science, Engineering or a related field
Strong production-grade programming skills in C# and Python, or equivalent
Solid understanding of distributed systems, such as networking, storage, concurrency and fault tolerance
Familiarity with AI engineering tooling, such as: LangGraph / LangChain Pydantic FastAPI MCP RAG pipelines Agentic workflows

Software Engineering

AI/ML/GenAI Engineering

Languages

English

Nice to Haves

Exposure to GPU scheduling or model-parallel inference (e.g. vLLM, TensorRT-LLM)
Contributions to open-source AI infrastructure projects

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