2026 - Internship, Machine Learning Engineer

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Paris, Île-de-France, FRA
In-Office
Internship
Financial Services
The Role
As a Machine Learning Engineer intern at QRT, you will design evaluation pipelines for LLMs, integrate models, and optimize workflows.
Summary Generated by Built In

Programme duration: 6 months, starting in 2026.   

Who qualifies: Final year students completing a Bachelor's, Master's.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.  

Over the years, QRT has invested in a global research and execution platform which has been deployed to cover all geographies and asset classes. This platform covers a broad spectrum from high to low frequency trading systems. We thrive at the intersection of cutting-edge technology, smart automation, and scalable processes, enabling us to move fast, think big, and deliver at scale.   

We are committed to identifying and developing exceptional talent, and are inviting a new cohort of outstanding individuals to join us in the year ahead. Our internship offers a stimulating, intellectually rigorous, and high-performance environment, where collaboration is key to success. You will work alongside and be mentored by industry-leading professionals, gaining invaluable experience and positioning yourself for the opportunity to secure a full-time graduate role upon successful completion of the program. 

 

Your future role at QRT 

As a Machine Learning Engineer at QRT, you will contribute to building the systems used to evaluate and compare LLMs across a range of tasks and datasets. Design evaluation pipelines, define scoring methodologies, and develop the services that run, track, and reproduce experiments at scale. Integrate new models into a unified framework and optimize evaluation workflows for performance and reliability.


Your present skillset 

  • Strong Python skills, with experience building reliable and maintainable systems.
  • Solid software engineering fundamentals, including APIs, testing, and modular design.
  • Experience working with data pipelines and experimentation workflows.
  • Excellent communication skills - you will interact directly with Traders and Researchers.  
  • Strong analytical and problem-solving skills, with a structured approach to ambiguous problems.
  • Interest in large language models (LLMs), machine learning systems, and evaluation methodologies.

Preferred qualifications (a plus): 

  • Experience with LLM tooling or frameworks (e.g. Hugging Face, OpenAI APIs, vLLM, or similar).
  • Familiarity with evaluation or benchmarking frameworks (e.g. lm-eval, HELM, or custom evaluation pipelines).
  • Experience building backend services (FastAPI, Flask, or similar) and job orchestration systems.
  • Exposure to distributed systems, parallel computing, or GPU-based workloads.
  • Experience with experiment tracking tools (e.g. MLflow, Weights & Biases) or similar systems.
  • Interest in model evaluation, robustness, and real-world performance of machine learning systems.

 

Interview Process 

  • Application - Submit your application online. We review applications on a rolling basis, so we recommend applying early to maximize your chances. 
  • Technical Assessment - Selected candidates will be invited to complete a coding challenge designed to evaluate core technical and problem-solving skills. 
  • Interviews - Shortlisted applicants will proceed to interviews, conducted either on-site or via Microsoft Teams. These will assess both your technical expertise and your alignment with our culture and values. 

 

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance. 


Skills Required

  • Strong Python skills
  • Solid software engineering fundamentals
  • Experience working with data pipelines and experimentation workflows
  • Excellent communication skills
  • Strong analytical and problem-solving skills

Qube Research & Technologies Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Qube Research & Technologies and has not been reviewed or approved by Qube Research & Technologies.

  • Wellbeing & Lifestyle Benefits Office amenities such as free meals, social events, and wellness-focused workspaces are highlighted in multiple locations. Cycle-to-work schemes and onsite classes in Europe further enhance day-to-day quality of life.
  • Leave & Time Off Breadth Two paid volunteer days and corporate donation matching were introduced firmwide. Some locations also cite generous annual leave allowances with options to buy additional days.
  • Healthcare Strength Private medical coverage and life insurance are called out for the UK. Job listings reference health insurance in various regions, though specifics differ by office.

Qube Research & Technologies Insights

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The Company
London
774 Employees

What We Do

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. We currently have multiple open positions on our website, please get in touch! Our commitments: https://www.qube-rt.com/commitments

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