Lead design and implementation of vector search features in Elasticsearch (including improving Lucene), work on vector similarity algorithms (HNSW, IVF, etc.), implement new algorithms or data structures, fix bugs, and collaborate with a globally distributed engineering and open source community. Requires strong Java skills, experience with vector databases and search at scale, distributed systems knowledge, and experience with related data storage technologies.

Join the globally distributed Elasticsearch Search team as a Principal Software Engineer to lead design and implementation of vector search features, improve vector similarity functionality (e.g., HNSW, IVF), fix bugs, and collaborate with a distributed open-source community. The role emphasizes strong Java, algorithms, data structures, concurrency, and distributed-systems skills and is based in/associated with Norway.

Principal Software Engineer role on the Elasticsearch Search team focused on designing and implementing vector similarity search features, improving existing vector search functionality, resolving bugs, and collaborating with a globally distributed engineering and open source community. The role emphasizes Java development, search algorithms (e.g., HNSW, IVF), data structures, concurrency, and performance at scale.

Lead development of vector search capabilities within Elasticsearch, designing and implementing new vector search features, improving existing functionality, fixing bugs, and collaborating with a globally distributed engineering and open-source community. Requires strong Java skills, experience with vector similarity techniques (e.g., HNSW, IVF) and working at scale with search or vector database systems.

Role involves working with researchers and ML engineers to design, prototype, and scale end-to-end training (pre-training, fine-tuning, RL) and data pipelines for robotics foundation models. Requires 5+ years experience in deep learning/distributed training, expertise in frameworks like PyTorch/JAX/NeMo, and familiarity with profiling and optimization. Location indicated as US (Santa Clara) with remote mentioned; listed salary range 152,000–287,500 USD/year depending on level.

Join Elasticsearch's globally distributed Search Relevance team to design and implement new search features, improve vector search capabilities (including HNSW/IVF and vector similarity), contribute to Lucene and Elasticsearch codebase, and collaborate with a distributed open-source community. The role emphasizes modern Java development, algorithm and data-structure work, and experience with search or vector databases.

Join the globally distributed Elasticsearch Search Relevance team as a Senior Software Engineer focused on vector search and search relevance. Responsibilities include designing and implementing new search features, improving relevance (including Lucene contributions), working with vector similarity algorithms (HNSW, IVF), and writing idiomatic modern Java in a distributed, open-source-oriented environment.

Join the globally distributed Elasticsearch Search Relevance team to design and implement vector search and relevance features, fix bugs, and improve search performance. Role emphasizes Java development, work with vector similarity algorithms (e.g., HNSW, IVF), collaboration on open source (GitHub), and contributions to Lucene/Elasticsearch. Location: Greece. Typical starting salary range indicated: €61,100–€96,600 EUR.

Join Elasticsearch's Search Relevance team to design and implement new search features, improve vector search capabilities (HNSW, IVF, vector similarity), work with Lucene, and write idiomatic modern Java on a globally distributed, open-source-focused team. Salary range provided in EUR.

Role involves designing and implementing new search features and enhancements in Elasticsearch (including contributions to Lucene), leading initiatives for vector search capabilities, working with algorithms like HNSW and IVF, collaborating with a globally distributed engineering and open-source community, and writing idiomatic modern Java.

Join the globally distributed Elasticsearch Search Relevance team to design and implement new search features, improve relevance, and fix issues in Elasticsearch/Lucene. The role emphasizes modern Java development, vector search algorithms (HNSW, IVF), working with large-scale search platforms and collaborating with a distributed open-source community.

Join the globally distributed Elasticsearch Search Relevance team to design and implement new search features, improve vector search capabilities, and resolve complex bugs. The role emphasizes Java development, work with vector similarity algorithms (e.g., HNSW, IVF), collaboration on open-source components like Lucene/Elasticsearch, and operating at scale in distributed systems.

Seeking a Senior Software Engineer to lead initiatives in Elasticsearch search relevance and vector search, design and implement search features, improve performance, and fix bugs. The role emphasizes modern Java development, experience with search/vector databases (HNSW, IVF), working in a distributed, asynchronous engineering environment, and collaboration on open source projects.

Role to design and implement new search features, improve search relevance (including work touching Lucene), contribute to vector search capabilities (HNSW, IVF), fix bugs, and collaborate with a globally distributed engineering team. Compensation range listed for Poland: 307,800 — 486,900 PLN (base salary).

Join the globally distributed Elasticsearch Search Relevance team to design and implement new search features, improve relevance (including contributions to Lucene), and work on vector search capabilities. The role requires strong Java skills, experience with search or vector databases (HNSW, IVF, etc.), algorithm and data-structure design, and collaborative development in distributed/open-source environments.

Elastic is hiring a Senior Software Engineer for the Elasticsearch - Search Relevance team to lead initiatives that improve search speed and relevance, contribute new search features and fixes (including work touching Lucene), and collaborate with a globally distributed engineering team. The role emphasizes expertise in search/vector databases and algorithms (e.g., HNSW, IVF), strong core Java skills, open-source collaboration, and autonomous, asynchronous distributed-team working.

Join the globally distributed Elasticsearch Search Relevance team to lead development of search features, improve Lucene, and work on vector search capabilities. Responsibilities include designing and implementing algorithms and data structures, fixing complex bugs, collaborating across a distributed team, and writing idiomatic modern Java. Required experience includes search or vector databases and familiarity with algorithms such as HNSW or IVF; experience with data storage technologies (Elasticsearch, Solr, PostgreSQL, MongoDB, Cassandra) and using AI tools to aid development is highlighted.

Conduct research to advance intelligent systems, co-develop models and harnesses to improve memory and general intelligence in VLM/LLM systems, publish results, and open-source reproducible code. Requires a PhD or equivalent and at least 6 years of relevant experience in machine learning, AI, computational statistics, or applied mathematics, with proficiency in Python and a track record of research publications.

Lead strategy and execution for NVIDIA's reinforcement-learning post-training frameworks and the systems they integrate with (inference, rollout, reward evaluation, orchestration). Role requires strong distributed-systems and ML-infrastructure experience, building multi-team execution plans, open-source collaboration, and hiring/managing engineering leaders. Requires MS/PhD or equivalent and 10+ years software engineering experience (with 4+ years managing software teams). Salary range listed: 272,000–431,250 USD per year.

Work across research, engineering, and product teams to develop and profile workflows and sample applications, benchmark and optimize GPU-accelerated computing and ML frameworks, and represent NVIDIA at scientific events. Requires BS/MS/PhD (or equivalent) and 5+ years building HPC/AI/ML software for physics and numerical simulations.