Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise.
We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows.
Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence.
Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London.
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.
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.
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.
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.
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.
Mercor is an Equal Opportunity Employer and is committed to providing equal opportunity to all employees and applicants. Mercor does not discriminate based on actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression, sexual orientation, marital status, military service and veteran status, disability, protected medical condition as defined by California or local law, genetic information, or any other characteristic protected by applicable federal, California, or local laws and ordinances. Further, Mercor prohibits discrimination, harassment, and retaliation based on any combination of protected characteristics. This Policy applies to all areas of employment, including recruitment, hiring, training, promotion, compensation, benefits, discipline, termination, and all other privileges, terms, and conditions of employment. This Policy and the law prohibit employment discrimination against any employee or applicant based on any legally protected status.