{"id":1302739,"url":"https://alion.io/job/naboo-software-engineer","title":"Software Engineer","company":{"id":3789798,"name":"Naboo","domain":"naboo.ai","url":"https://alion.io/company/naboo-4","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenSearch","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Reranking","optional":false},{"name":"Memgraph","optional":true},{"name":"Neo4j","optional":true}],"status":"live","first_seen_at":"2026-09-08T00:00:00Z","employer_posted_date":"2026-09-08","last_verified_at":"2026-09-30T02:37:44Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"Deep systems and data engineering, not prompt engineering - the hard problems are retrieval precision, latency, and context density at enterprise scale.Position Overview\nIn this role, you will build the core engine underneath Naboo: high-throughput indexing pipelines, graph traversal algorithms, hybrid retrieval (lexical + vector + graph), and low-latency query execution. This is deep systems and data engineering, not prompt engineering - the hard problems are retrieval precision, latency, and context density at enterprise scale.\nMain Responsibilities\nCore Engine & Real-Time Data Pipelines: Design, build, and scale data pipelines, live ETL processes, and indexing engines that continuously pull and unify data from multiple customer integrations into a single knowledge graph, in real time - plus the graph-traversal systems that transform enterprise artifacts into that unified graph.\nRetrieval & Relevance: Optimize hybrid retrieval (BM25, dense embeddings, graph expansion, reranking) to maximize precision and minimize token footprint for downstream agent execution.\nEvaluation & Benchmarking: Build offline and online eval frameworks to benchmark retrieval accuracy, latency, and agent task success against real enterprise workloads.\nProduction Ownership: Share end-to-end ownership of core systems - from schema design and implementation to deployment, observability, and production reliability.\nTechnical Direction: Collaborate closely with the team on architectural decisions as agent paradigms (MCP, multi-agent coordination, context caching) rapidly evolve.\nRequirements\n3-5 years of experience building high-performance backend systems, distributed data pipelines, or data-intensive applications.\nPractical experience with retrieval & search: embeddings, vector search, lexical search (BM25 / Lucene / OpenSearch), and reranking trade-offs.\nAgentic SDLC practitioner: hands-on experience working in an AI-native development environment - actively using AI coding agents (Claude Code, Cursor, etc.) in your daily engineering workflow, designing with agent-first primitives, and dogfooding the paradigm we build for our customers.\nProduct velocity: thrives in a fast-paced environment with high ownership, taking ambiguous technical problems from 0 to production. We're looking for people who identify problems and propose solutions on their own, rather than waiting to be told what to do. We encourage people to bring different ideas and value people who are not afraid to voice their opinion.\nNice to Have\nExperience with graph databases (Neo4j, Memgraph, FalkorDB) or knowledge graph representations.\nFamiliarity with the Model Context Protocol (MCP) or agentic execution frameworks.\nBackground in search relevance, learning-to-rank, or synthetic evaluation generation.\nEarly-stage startup experience.\n• B.Sc. in Computer Science, or equivalent hands-on experience at strong engineering companies.","description_format":"text","description_chars":2917,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","LLM & Generative AI","AI Agents"],"lifecycle":[{"event":"open","at":"2026-09-26T12:17:20Z"}],"liveness":{"score":31,"band":"fade","label":"Fading","p_open":0.9,"p_active":0.634,"p_room":0.55,"age_days":21,"expected_fill_days":17,"reasons":["conf:61","win:tail"],"computed_at":"2026-09-29T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/naboo-software-engineer","json_url":"https://alion.io/job/naboo-software-engineer.json","meta":{"generated_at":"2026-09-30T03:44:13Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2599,"day_limit":5000,"remaining_today":2401,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}