Internship - Search Machine Learning Engineer
Role Overview
This is an internship role for a Search Machine Learning Engineer, ideal for a junior-level candidate or student, working closely with experienced engineers on a team focused on AI and search technologies. Day-to-day responsibilities include contributing to experiments to improve search quality, designing and implementing components of the search platform such as retrieval and ranking models, and supporting deployment and monitoring of models in a scalable way. The hire will have a direct impact on enhancing search technologies and user experience through hands-on work on RAG pipelines and collaboration with cross-functional teams.
Perks & Benefits
The internship is full-time and in-person in the London office, offering hands-on experience in a fast-paced environment with opportunities to learn from senior engineers and collaborate with Data, AI, Infrastructure, and Product teams. It provides career growth through exposure to production ML best practices and the chance to work on advanced search technologies that directly impact users. While remote work is not specified for the internship, the company's remote job listing suggests a flexible culture, and interns can expect mentorship and a collaborative, innovative work setting.
Full Job Description
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
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