weaviate - Applied Research Intern

• Programming Languages: Experience with statically typed languages such as C/C++, Go, Rust, or similar. (Note that Weaviate is written in Go; prior Go experience is preferred but not strictly required) • Preferred Coursework: Database-related courses, Information-Retrieval-related courses, System Design, adv. Algos and Data structures • A strong sense of ownership of your work, ability to work independently • Kindness and excellent communication skills • Eligibility for an internship: • For U.S. Citizens in the U.S.: • You must have all necessary documentation to be legally employed in the United States (e.g. Social Security Number) before commencing the internship. • For Foreign Students in the U.S.: • You must have a valid student visa (e.g. F-1) in place at the time of application. • You are responsible for securing the appropriate work authorization (e.g. CPT, OPT) prior to the start of your internship. This includes coordinating with your educational institution to obtain the necessary approvals and documentation. • Please note that employment is contingent upon receiving proper authorization, and you will be required to provide proof of work eligibility before commencing the internship. • For All Other Countries: • You must have the right to work in the country you are currently living, including a residence title (visa or residence permit) if required. • You must have the required paperwork arranged in order to be able to start your employment (e.g. social security number, bank account, etc.) • EXAMPLE PROJECTS • Below is a list of example projects by their respective area. If you have your own project ideas that fit with the general theme of projects, please let us know. When applying, please specify your top 3 project preferences. • STORAGE & NETWORKING • Improve Filter performance at scale (roaring bitmaps, inverted indexes, etc) • Design and implement new index types that can be used with various storage backends (NVMe, network disk, cloud storage) • Optimize hot-path code in the storage engine (Memory allocations and usage, Disk I/O, SIMD, etc.) • Use start-of-the-art data structures to improve the performance and storage footprint of existing index types • Improve inter-node network communication by using lightweight and optimized network protocols. • VECTOR INDEXES / GRAPH ALGORITHMS • Improve query performance by (approximating distances via FINGER, early exit conditions, etc.) • Improve range filter performance for vector search (time-series vector search, SeRF, etc) • Improve general filtered vector-search performance (ACORN, etc.) • Design and implement a new cloud-storage-based vector index for specific scenarios • INFORMATION RETRIEVAL, RAG, END-TO-END APPLICATIONS • Fine-tune embedding models and rerankers for specific domains (adaptive fine-tuning, synthetic data generation, ColBERT) • Video embedding models and natural language search over video. (CLIP, contrastive learning) • Develop new hybrid recommendations systems, combining collaborative filtering with content-based approaches (autoencoders, GNN, Matrix completion) • Optimize Model Inference (vLLM, TensorRT) • Weaviate as a library: Investigate and develop library usage of Weaviate via focusing on embedding Weaviate into the popular Golang-based Ollama inference service