Asks for

The ad says“Excellent systems fundamentals: distributed systems, data modeling, and API design at scale.”
The ad says“Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.”
The ad says“Excellent systems fundamentals: distributed systems, data modeling, and API design at scale.”
The ad says“Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.”
The ad says“Excellent systems fundamentals: distributed systems, data modeling, and API design at scale.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.”
The ad says“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.”
The ad says“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.”
The ad says“Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.”
The ad says“Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.”
The ad says“Familiarity translating natural-language questions into structured search queries (query understanding, semantic parsing, or LLM-assisted query generation).”
The ad says“Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.”
The ad says“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.”
The ad says“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.”

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.

About the Role

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 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 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.

What You'll Do

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.

What We're Looking For

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.

Nice to Have

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.

Why Mercor

Impact: Own the code search systems that decide how well Mercor finds, routes, and evaluates the code tasks training the world's frontier models, at a company scaling faster than almost any in its category.
Ownership: Org-wide technical scope with a direct line to engineering leadership and real authority over technical direction.
Learning: Work alongside world-class engineers, product leaders, and AI researchers building at the frontier of AI.
Growth: Shape the engineering organization itself—its standards, its architecture, and its next generation of technical leaders.

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

Mercor is an equal opportunity employer. We work in-person five days a week in our San Francisco office.