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Paid Internship
Work Mode
Time Spent
Required Degree
Duration

4Open Positions

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  • Research Engineer Intern

    hiverge
    Cambridge, United Kingdom
    Found 2 months ago
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    NXP Semiconductors
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    AMD
    Belfast, United Kingdom
    Found 1 month ago
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    Technische Universität Berlin
    Berlin, Germany
    Found 1 month ago
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    Belfort, France
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Research Engineer Intern

hiverge
Found 2 months ago
Location
Cambridge, United Kingdom
Duration (Months)
3 Months
Time
Full-time
Work Mode
On-site
Salary
competitive compensation
Visa Help
Not disclosed
Last Verified
2 months ago

Education

  • Master

Skills & Qualifications

Technical Skills

  • Python
  • PyTorch
  • JAX
  • LLM tooling
  • evaluation frameworks
  • performance profiling

Soft Skills

  • strong reproducibility
  • observability
  • better efficiency
  • implement new ideas

Job Description

This role designs, trains, and evaluates LLM‑centric systems. The work blends implementation, experimentation, and benchmarking. As a Research Engineer Intern you will: Support the development of scalable experimentation foundations with strong reproducibility and observability Help create and improve robust prompt and code-selection tooling. Assist in optimizing our system’s inference and infrastructure for better efficiency. Work with the team (ML engineers and researchers) to implement new ideas. Engage with the research community to advance the state of algorithmic discovery.

Requirements

  • Masters degree in Computer Science, Engineering, related field, or equivalent experience.
  • Strong coding skills in Python and experience with PyTorch/JAX and modern LLM tooling for inference and fine‑tuning.
  • Experience in applied research engineering, demonstrated by impactful academic projects or internships in LLMs, search/RL, or compilers/verification.
  • Exposure to evaluation frameworks, unit/property‑based testing, and performance profiling.

Related Field

  • AI & Machine Learning

Related Subfield

  • AI Research

Nice to Haves

  • Contributions to open research or community tooling.
  • Familiarity with algorithm design, and practical understanding of reinforcement learning, evolutionary methods and optimization methods.
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