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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Search Systems - Code Data - **Company:** Mercor, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Experienced - **Salary:** $10,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Systems Engineering, Automated Storage and Retrieval Systems, Cloud Engineering, Apache Lucene, Encodings, Cursor, Programming Tools, Distributed Systems, Elasticsearch, Search Algorithms, Parsing, Recommender Systems, Microsoft Copilot, Search Technologies, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Claude Code, HNSW (Hierarchical Navigable Small World), Code Structure, Production Code, FAISS, Api Design, OpenSearch - **Published:** October 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/software-engineer-search-systems-code-data-san-francisco-ca--8dc75209-3737-4b92-b281-9724b3d404d8 ## About the Role Software Engineering, Systems Engineering, Training/Teaching, Artificial Intelligence (AI), Human Intelligence (HUMINT), Benchmarking, Fortune 500 Customers, Category Development, Software Engineering, Algorithms, * 8+ years of professional software engineering experience, including 3+ years operating at a Senior level or above, with a Staff-level track record of org-wide technical impact. * Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking. * Good to have but not required: Experience with code search or code understanding-retrieval over code, code embeddings, or working with code-specific models-and an appreciation for why matching similar code tasks is harder than matching text. * Strong understanding of the search algorithms and index internals-vector/ANN indices (e.g. HNSW, IVF, product quantization), inverted indices, and engines such as Elasticsearch/OpenSearch, Lucene, FAISS, or vector databases. * A track record of making the right cost-vs-speed tradeoffs: latency budgets, throughput, memory footprint, and infrastructure spend on high-QPS systems. * Familiarity translating natural-language questions into structured search queries (query understanding, semantic parsing, or LLM-assisted query generation). * Excellent systems fundamentals: distributed systems, data modeling, and API design at scale. * Demonstrated technical leadership and mentorship-you've helped junior and senior engineers grow and level up an engineering team. * Genuine excitement for agentic development and new technology, fluency with modern AI dev tools (e.g. Claude Code, Cursor, Copilot), and a deep passion for writing great code. * Excellent communication-able to make complex tradeoffs legible to both engineers and leadership. Strong opinions, loosely held. High ownership, pragmatism, and a bias toward shipping., * Experience training or fine-tuning code embedding models or code-specific LLMs. * Experience with learning-to-rank, semantic search, or recommendation systems in production. * Familiarity with LLM-based retrieval, RAG patterns, and model routing/selection. * Background operating latency-critical services on modern cloud and orchestration infrastructure. ## Description As an engineer, your impact extends well beyond your own commits. You'll set the technical direction the rest of the org builds on, mentor and grow the engineers around you, and raise the bar for how we build., * Solve the hard problem of identifying similar code tasks-retrieval that captures code structure, semantics, intent, and difficulty rather than surface text. * Build the system that identifies and selects the correct code-specific models for a given task, and routes tasks to the right model. * Design natural-language-to-query translation that turns NLP questions into precise search over code and tasks. * Design and operate the indexing pipeline so the task index stays fresh and consistent as new tasks, solutions, and results arrive continuously-balancing incremental updates, full rebuilds, and real-time ingestion. * Make the cost-and-speed tradeoffs that keep search fast and economical at scale: embedding dimensionality and quantization, ANN index choice and parameters, caching, sharding, and serving infrastructure. * Build the systems and evaluation harnesses that let us continuously evolve embeddings, models, and search quality-safely swapping in new code models, re-embedding corpora, and A/B testing relevance as SOTA advances. * Define and drive the long-term technical strategy for code retrieval across the organization, and lead the highest-stakes design reviews. * Establish evaluation metrics, offline/online testing, and quality guardrails so search improvements are measurable and regressions are caught before they ship. * Stay deeply hands-on: prototype critical systems, ship production code, and unblock teams on their hardest retrieval and infrastructure problems. * Mentor and grow engineers-junior and senior-through design reviews, pairing, and clear technical writing, raising the technical bar across the org. * Partner with product, researchers, and engineering leadership on build-vs-buy decisions, platform investments, and technical hiring.