Internship - Search Machine Learning Engineer
Build and improve retrieval, ranking, classification, and RAG systems for advanced search. The internship involves developing and evaluating machine learning models with Python and frameworks such as PyTorch, TensorFlow, or JAX, while collaborating with cross-functional engineering teams.
About the job
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 and evaluate 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.
Requirements
- Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems.
- Experience with Python and common machine learning frameworks, such as PyTorch, TensorFlow, or JAX, through academic, open-source, or personal projects.
- Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment.
Nice-to-haves
- Familiarity with evaluating model quality using offline metrics and/or A/B testing.
- Previous experience through internships, research, or significant projects working on search, recommendation, or natural language processing.
- Experience with Rust.
Compensation and Benefits
- Internship program lasting 12–24 weeks.
- Full-time, in-person internship in the London office.
Skills
Machine Learning, Statistics, Information Retrieval, Ranking Models, Recommender Systems, Python, PyTorch, TensorFlow, JAX, Natural Language Processing, A/B Testing, Rag Pipelines, LLMs, Rust, Model Deployment
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