Software Engineer II, Neural Retrieval
4 Tage altAngaben zum Job
| Firma | |
| Kategorie | IT, Ingenieurswesen | Pensum | 100% |
| Lohn (geschätzt) | CHF 88'000 – 112'000 / Jahr |
| Einsatzort | Zürich |
Job-Inhalt
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 1 year of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
- 1 year of experience with data structures and algorithms.
- 1 year of experience implementing core ML concepts.
Preferred qualifications:
- Master's degree in Computer Science or a related technical field.
- Experience with training and evaluating AI models (embedding and models).
- Experience developing LLM-based products.
- Experience with model training and eval tools, indexing and serving infrastructure.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
As part of Search Platforms, the Neural Retrieval team's mission is to provide possible inputs to Large Language Models (LLM) Search products.
We are working directly with client teams to build Retrieval-augmented generation (RAG) solutions that scale across Google, based on the latest LLM advances in embeddings and combining them with structured data approaches (like SQL-based retrieval). The team is looking to harness the power of agents and latest LLM frontier to scale to new domains, while significantly advancing developer speed, quality and cost of retrieving the most helpful content. We are building on top of and working closely with Core IR (Retrieval Engine) and Gemini Encoder Heavy and Embeddings to deliver impact across Search.
Responsibilities
- Write product or system development code. Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Improve embedding retrieval quality with better training data, improved and creative recipes.
- Help build a retrieval system that scales to these domains with minimal engineering effort, allows quick evaluation and user facing turnaround time.