Internship - Search Machine Learning Engineer
At a glance
Junior Machine Learning Engineering role at Perplexity. London · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Help test ways to raise search quality through improved models, data choices and evaluation tools, with senior engineers guiding the work
- Build parts of the search platform and model stack for retrieval, ranking and classification
- Train and assess models, including LLM-based methods, for retrieval, ranking and classification tasks
- Assist with deploying and tracking search and ranking models, and develop RAG pipelines for grounded answers
What you bring
- A solid grounding in machine learning and statistics, supported by study or projects in retrieval, ranking or recommender systems
- Project experience using Python and a common machine-learning framework such as PyTorch, TensorFlow or JAX
- Curiosity, initiative and ownership, with a readiness to learn in a fast-moving setting
Who this fits
This full-time internship suits someone looking to build production machine-learning experience alongside senior engineers and partner teams. It runs for twelve to twenty-four weeks and requires working in person at the London office.
From the employer
Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information.
Internship program: 12 - 24 weeks, full-time, in-person in the London office.
Responsibilities:
Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers.
Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models.
Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks.
Support deployment and monitoring of search and ranking models in a scalable and performant way.
Help build and iterate on RAG pipelines for grounding and answer generation.
Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML.
Qualifications:
Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems.
Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects.
Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required.
Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required.
Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment
Experience with Rust will be a plus