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About the Role
Own the architecture and algorithms for searching across code tasks, finding similar tasks, routing them to the right models, and translating natural-language questions into precise retrieval. This hands-on technical leadership role involves designing and building retrieval systems, evolving embeddings and models as new state-of-the-art code models arrive, setting technical direction, and mentoring engineers.
Responsibilities
- 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 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.
Requirements
- 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.
- 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.
Skills
- Dense-embedding retrieval
- Lexical scoring (BM25/TF-IDF)
- Hybrid ranking
- Re-ranking
- Vector/ANN indices (e.g. HNSW, IVF, product quantization)
- Inverted indices
- Elasticsearch/OpenSearch
- Lucene
- FAISS
- Vector databases
- Distributed systems
- Data modeling
- API design
- Claude Code
- Cursor
- Copilot
- Training or fine-tuning code embedding models
- Training or fine-tuning code-specific LLMs
- Learning-to-rank
- Semantic search
- Recommendation systems
- LLM-based retrieval
- RAG patterns
- Model routing/selection
- Cloud infrastructure
- Orchestration infrastructure
Location
- San Francisco
- NYC
- London
Work Type
- In-person
Experience Level
- Staff+
- Senior level or above
Benefits
- Generous equity grant vested over 4 years
- Up to $15K relocation bonus (if moving to the Bay Area)
- A $10K housing bonus (if you live within 0.5 miles of our office)
- A $1.5K monthly stipend for meals
- Free Equinox membership
- Health insurance
About the Company
- 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.
Equal Opportunity
- Mercor is an equal opportunity employer.