At a Glance
- Tasks: Design and build a unified benchmarking system for multi-step LLM evaluation.
- Company: Join Callosum, a pioneering company in AI research and development.
- Benefits: Equity, private healthcare, visa sponsorship, and relocation support.
- Other info: Exciting opportunity to work in London with a collaborative team.
- Why this job: Lead innovative projects and make a significant impact in AI evaluation.
- Qualifications: PhD-level research experience in relevant fields.
The predicted salary is between 80100 - 97900 £ per year.
Callosum is seeking a Ph D-level researcher to own and build a unified benchmarking system for multi-step LLM work.
You will design task suites, run sandboxed grading with real commits and traces, and base decisions on reproducible results rather than model scores.
Lead external benchmark co-publications, enforce contamination controls, and drive open, scalable evaluation.
This London-based, in-person role offers equity, private healthcare, visa sponsorship, and relocation support.
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Benchmarking Systems Engineer for LLM Evaluation employer: Callosum
At Callosum, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. Our team thrives in a dynamic environment where tackling complex challenges is not just encouraged but celebrated, offering ample opportunities for professional growth and development. Located at the forefront of AI technology, we provide our employees with access to cutting-edge resources and a supportive community that values diverse perspectives and ideas.
StudySmarter Expert Advice🤫
We think this is how you could land Benchmarking Systems Engineer for LLM Evaluation
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We think you need these skills to ace Benchmarking Systems Engineer for LLM Evaluation
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!
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