Why This Role Stands Out
This internship offers a unique opportunity to contribute to cutting-edge AI development alongside world-class researchers from DeepMind and Cambridge, gaining invaluable experience in LLM-centric systems and algorithmic discovery. You'll thrive here if you're passionate about AI, possess strong Python and PyTorch/JAX skills, and are eager to implement, experiment, and benchmark innovative solutions. Apply now to be part of shaping transformative technologies!
Quick Overview
Job Description
Who we are
At Hiverge, our mission is to build algorithmic superintelligence: an AI discovery engine that automatically writes algorithms for the most critical optimisation problems.
Our founding team is composed of world-class researchers from Google DeepMind and University of Cambridge, having co-developed AlphaTensor, FunSearch, and Nobel-prize winning AlphaFold. At Hiverge, you will be working with the brightest minds in AI, algorithms, and mathematics.
If you’re passionate about AI and eager to shape transformative technologies, we’d love to hear from you.
About this role
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.
Qualifications
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.
What sets the candidate apart
Contributions to open research or community tooling.
Familiarity with algorithm design, and practical understanding of reinforcement learning, evolutionary methods and optimization methods.
We offer competitive compensation and a stimulating work environment, with the opportunity to contribute to the future of generative AI.
Outstanding interns may be considered for a full-time Research Engineer position upon completion of the internship.
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