Staff Software Engineer, Search Systems - Code Data

About Mercor Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.   Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

Some of the most valuable work on Mercor's platform is code —the tasks, problems, and solutions that train and evaluate the world's frontier coding models. As a Staff Software Engineer for Code Search & Retrieval , you'll own the architecture and algorithms behind how we search across code tasks: finding similar tasks, routing them to the right models, and turning natural-language questions into precise retrieval. Searching over code is a genuinely hard problem. Identifying similar code tasks —not just similar text—requires retrieval that understands structure, semantics, intent, and difficulty, far beyond what generic embeddings capture. You'll own that end to end: the vision is to search across code tasks at scale, identify and select the correct code-specific models for a given task, and translate NLP questions into search queries that return the right code and tasks. This is a hands-on technical leadership role, not a management role. You'll design and build the retrieval systems that combine dense (code) embeddings and lexical (BM25) signals , and make the tradeoffs that let search stay fast and affordable at scale. Because Mercor operates at the frontier of data and models, you'll also own the hardest part of the problem: continuously evolving embeddings, models, and search quality as newer, state-of-the-art code models arrive—without regressing what already works. As a Staff+ 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.

Own the architecture of Mercor's code search and retrieval systems end to end— hybrid retrieval combining dense code embeddings and BM25 , candidate generation, ranking, and re-ranking over code tasks. 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