At a Glance
- Tasks: Design, train, and evaluate cutting-edge AI systems while collaborating with top researchers.
- Company: Join Hiverge, a pioneering AI company with a mission to revolutionise algorithmic discovery.
- Benefits: Competitive pay, stimulating work environment, and potential for full-time role after internship.
- Other info: Engage with the research community and contribute to impactful projects.
- Why this job: Shape the future of generative AI and work alongside world-class experts in the field.
- Qualifications: Masters in Computer Science or related field, strong Python skills, and experience with LLMs.
The predicted salary is between 20000 - 30000 £ per year.
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.
Research Engineer Intern in Cambridge employer: Hiverge
At Hiverge, we pride ourselves on being an exceptional employer, offering a stimulating work environment where innovation thrives. Our team comprises world-class researchers and engineers, providing unparalleled opportunities for professional growth and collaboration on groundbreaking AI technologies. With competitive compensation and generous benefits, we empower our employees to shape the future of generative AI while working alongside some of the brightest minds in the industry.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer Intern in Cambridge
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We think you need these skills to ace Research Engineer Intern in Cambridge
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Hiverge, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Hiverge. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Hiverge
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Hiverge!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
